diff --git a/python_package/examples/plot_real_time/downsampling.py b/python_package/examples/plot_real_time/downsampling.py
deleted file mode 100644
index 6072ccd90..000000000
--- a/python_package/examples/plot_real_time/downsampling.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import time
-
-from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
-from brainflow.data_filter import DataFilter, AggOperations
-
-
-def main():
-    BoardShim.enable_dev_board_logger()
-
-    # use synthetic board for demo
-    params = BrainFlowInputParams()
-    params.mac_address =   '84:BA:20:6E:3C:1E'#'60:77:71:74:E6:B7'#
-    params.timeout  = 40  # board = BoardShim(BoardIds.SYNTHETIC_BOARD.value, params)
-    board = BoardShim(59, params)
-    board.prepare_session()
-    board.start_stream()
-    BoardShim.log_message(LogLevels.LEVEL_INFO.value, 'start sleeping in the main thread')
-    time.sleep(10)
-    data = board.get_board_data(20)
-    print(data)
-    board.stop_stream()
-    board.release_session()
-
-    # eeg_channels = BoardShim.get_eeg_channels(BoardIds.SYNTHETIC_BOARD.value)
-    eeg_channels = BoardShim.get_eeg_channels(59)
-    # demo for downsampling, it just aggregates data
-    for count, channel in enumerate(eeg_channels):
-        print('Original data for channel %d:' % channel)
-        print(data[channel])
-        if count == 0:
-            downsampled_data = DataFilter.perform_downsampling(data[channel], 3, AggOperations.MEDIAN.value)
-        elif count == 1:
-            downsampled_data = DataFilter.perform_downsampling(data[channel], 2, AggOperations.MEAN.value)
-        else:
-            downsampled_data = DataFilter.perform_downsampling(data[channel], 2, AggOperations.EACH.value)
-        print('Downsampled data for channel %d:' % channel)
-        print(downsampled_data)
-
-
-if __name__ == "__main__":
-    main()