How to use the hdmf.data_utils.DataChunkIterator function in hdmf

def test_write_dataset_datachunkiterator_with_compression(self):
        a = np.arange(30).reshape(5, 2, 3)
        aiter = iter(a)
        daiter = DataChunkIterator.from_iterable(aiter, buffer_size=2)
        wrapped_daiter = H5DataIO(data=daiter,
                                  compression='gzip',
                                  compression_opts=5,
                                  shuffle=True,
                                  fletcher32=True)
        ts = TimeSeries('ts_name', wrapped_daiter, 'A', timestamps=np.arange(5))
        self.nwbfile.add_acquisition(ts)
        with NWBHDF5IO(self.path, 'w') as io:
            io.write(self.nwbfile, cache_spec=False)
        with File(self.path, 'r') as f:
            dset = f['/acquisition/ts_name/data']
            self.assertEqual(dset.shape, a.shape)
            self.assertListEqual(dset[:].tolist(), a.tolist())
            self.assertEqual(dset.compression, 'gzip')
            self.assertEqual(dset.compression_opts, 5)
            self.assertEqual(dset.shuffle, True)

def test_write_dataset_datachunkiterator_data_and_time(self):
        a = np.arange(30).reshape(5, 2, 3)
        aiter = iter(a)
        daiter = DataChunkIterator.from_iterable(aiter, buffer_size=2)
        tstamps = np.arange(5)
        tsiter = DataChunkIterator.from_iterable(tstamps)
        ts = TimeSeries('ts_name', daiter, 'A', timestamps=tsiter)
        self.nwbfile.add_acquisition(ts)
        with NWBHDF5IO(self.path, 'w') as io:
            io.write(self.nwbfile, cache_spec=False)
        with File(self.path, 'r') as f:
            dset = f['/acquisition/ts_name/data']
            self.assertListEqual(dset[:].tolist(), a.tolist())

def test_dci_data(self):

        def generator_factory():
            return (i for i in range(100))

        data = DataChunkIterator(data=generator_factory())
        ts1 = TimeSeries('test_ts1', data,
                         'grams', starting_time=0.0, rate=0.1)
        with self.assertWarnsRegex(UserWarning, r'The data attribute on this TimeSeries \(named: test_ts1\) has no '
                                   '__len__'):
            self.assertIs(ts1.num_samples, None)
        for xi, yi in zip(data, generator_factory()):
            assert np.allclose(xi, yi)

def test_dci_data_arr(self):

        def generator_factory():
            return (np.array([i, i+1]) for i in range(100))

        data = DataChunkIterator(data=generator_factory())
        ts1 = TimeSeries('test_ts1', data,
                         'grams', starting_time=0.0, rate=0.1)
        # with self.assertWarnsRegex(UserWarning, r'.*name: \'test_ts1\'.*'):
        with self.assertWarns(UserWarning):
            self.assertIs(ts1.num_samples, None)
        for xi, yi in zip(data, generator_factory()):
            assert np.allclose(xi, yi)

# Open the file and read the next chunk
        newfp = np.memmap(filename, dtype=dtype, mode='r', shape=shape)
        curr_data = newfp[i:(i + 1), ...][0]
        del newfp  # Reopen the file in each iterator to prevent accumulation of data in memory
        yield curr_data
    return


####################
# Step 2: Wrap the generator in a DataChunkIterator
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#

from hdmf.data_utils import DataChunkIterator

data = DataChunkIterator(data=iter_largearray(filename='basic_sparse_iterwrite_testdata.npy',
                                              shape=datashape),
                         maxshape=datashape,
                         buffer_size=10)   # Buffer 10 elements into a chunk, i.e., create chunks of shape (10,10)


####################
# Step 3: Write the data as usual
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#

write_test_file(filename='basic_sparse_iterwrite_largearray.nwb',
                data=data)

####################
# .. tip::
#

def __init__(self, **kwargs):
        name, data, electrodes = popargs('name', 'data', 'electrodes', kwargs)
        timestamps = getargs('timestamps', kwargs)
        if not (isinstance(data, TimeSeries) or isinstance(timestamps, TimeSeries)):
            if not (isinstance(data, DataChunkIterator) or isinstance(timestamps, DataChunkIterator)):
                if len(data) != len(timestamps):
                    raise Exception('Must provide the same number of timestamps and spike events')
            else:
                # TODO: add check when we have DataChunkIterators
                pass
        super(SpikeEventSeries, self).__init__(name, data, electrodes, **kwargs)

while(x < 0.5 and num_chunks < max_chunks):
        val = np.asarray([sin(random() * 2 * pi) for i in range(chunk_length)])
        x = random()
        num_chunks += 1
        yield val
    return


####################
# Step 2: Wrap the generator in a DataChunkIterator
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#

from hdmf.data_utils import DataChunkIterator

data = DataChunkIterator(data=iter_sin(10))

####################
# Step 3: Write the data as usual
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Here we use our wrapped generator to create the data for a synthetic time series.

write_test_file(filename='basic_iterwrite_example.nwb',
                data=data)

####################
# Discussion
# ^^^^^^^^^^
# Note, we here actually do not know how long our timeseries will be.

print("maxshape=%s, recommended_data_shape=%s, dtype=%s" % (str(data.maxshape),

def __init__(self, **kwargs):
        name, data, electrodes = popargs('name', 'data', 'electrodes', kwargs)
        timestamps = getargs('timestamps', kwargs)
        if not (isinstance(data, TimeSeries) and isinstance(timestamps, TimeSeries)):
            if not (isinstance(data, DataChunkIterator) and isinstance(timestamps, DataChunkIterator)):
                if len(data) != len(timestamps):
                    raise Exception('Must provide the same number of timestamps and spike events')
            else:
                # TODO: add check when we have DataChunkIterators
                pass
        super(SpikeEventSeries, self).__init__(name, data, electrodes, **kwargs)

for ch in curr_ids])
            if len(np.unique(gains)) == 1:  # if all gains are equal
                scalar_conversion = np.unique(gains)[0] * 1e-6
                channel_conversion = None
            else:
                scalar_conversion = 1.
                channel_conversion = gains * 1e-6

            def data_generator(recording, channels_ids):
                #  generates data chunks for iterator
                for id in channels_ids:
                    data = recording.get_traces(channel_ids=[id]).flatten()
                    yield data

            data = data_generator(recording=recording, channels_ids=curr_ids)
            ephys_data = DataChunkIterator(data=data, iter_axis=1)
            acquisition_name = es['name']

            # To get traces in Volts = data*channel_conversion*conversion
            ephys_ts = ElectricalSeries(
                name=acquisition_name,
                data=ephys_data,
                electrodes=electrode_table_region,
                starting_time=recording.frame_to_time(0),
                rate=rate,
                conversion=scalar_conversion,
                channel_conversion=channel_conversion,
                comments='Generated from SpikeInterface::NwbRecordingExtractor',
                description='acquisition_description'
            )
            nwbfile.add_acquisition(ephys_ts)