How to use the brainflow.data_filter.DataFilter.read_file function in brainflow

data = board.get_board_data () # get all data and remove it from internal buffer
    board.stop_stream ()
    board.release_session ()

    # demo how to convert it to pandas DF and plot data
    eeg_channels = BoardShim.get_eeg_channels (args.board_id)
    df = pd.DataFrame (np.transpose (data))
    print ('Data From the Board')
    print (df.head ())
    plt.figure ()
    df[eeg_channels].plot (subplots = True)
    plt.savefig ('before_processing.png')

    # demo for data serialization
    DataFilter.write_file (data, 'test.csv', 'w')
    restored_data = DataFilter.read_file ('test.csv')
    restored_df = pd.DataFrame (np.transpose (restored_data))
    print ('Data From the File')
    print (restored_df.head ())

    # demo how to perform signal processing
    for count, channel in enumerate (eeg_channels):
        if count == 0:
            DataFilter.perform_bandpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 15.0, 6.0, 4, FilterTypes.BESSEL.value, 0)
        elif count == 1:
            DataFilter.perform_bandstop (data[channel], BoardShim.get_sampling_rate (args.board_id), 5.0, 1.0, 3, FilterTypes.BUTTERWORTH.value, 0)
        elif count == 2:
            DataFilter.perform_lowpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 9.0, 5, FilterTypes.CHEBYSHEV_TYPE_1.value, 1)
        elif count == 3:
            DataFilter.perform_highpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 3.0, 4, FilterTypes.BUTTERWORTH.value, 0)

    df = pd.DataFrame (np.transpose (data))

data = board.get_board_data () # get all data and remove it from internal buffer
    board.stop_stream ()
    board.release_session ()

    # demo how to convert it to pandas DF and plot data
    eeg_channels = BoardShim.get_eeg_channels (args.board_id)
    df = pd.DataFrame (np.transpose (data))
    print ('Data From the Board')
    print (df.head (10))
    plt.figure ()
    df[eeg_channels].plot (subplots = True)
    plt.savefig ('before_processing.png')

    # demo for data serialization
    DataFilter.write_file (data, 'test.csv', 'w')
    restored_data = DataFilter.read_file ('test.csv')
    restored_df = pd.DataFrame (np.transpose (restored_data))
    print ('Data From the File')
    print (restored_df.head (10))

    # demo how to perform signal processing
    for count, channel in enumerate (eeg_channels):
        # filters work in-place
        if count == 0:
            DataFilter.perform_bandpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 15.0, 6.0, 4, FilterTypes.BESSEL.value, 0)
        elif count == 1:
            DataFilter.perform_bandstop (data[channel], BoardShim.get_sampling_rate (args.board_id), 5.0, 1.0, 3, FilterTypes.BUTTERWORTH.value, 0)
        elif count == 2:
            DataFilter.perform_lowpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 9.0, 5, FilterTypes.CHEBYSHEV_TYPE_1.value, 1)
        elif count == 3:
            DataFilter.perform_highpass (data[channel], BoardShim.get_sampling_rate (args.board_id), 3.0, 4, FilterTypes.BUTTERWORTH.value, 0)
        elif count == 4: