input: Write a function to calculate the moving average of a time-series data using a specified window size and return the list of moving averages. If the window size is greater than the length of the data points, return an empty list. If the input window size is 0, return the original list.
  
  def moving_average(data_points, window):
      moving_averages = []
      if window == 0:
          return data_points
      if window > len(data_points):
          return moving_averages
      for index in range(len(data_points)):
          if index + 1 >= window:
              window_data = data_points[index + 1 - window:index + 1]
              average = sum(window_data) / window
              moving_averages.append(average)
      return moving_averages
  
  window = 3
  time_series = [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
  result = moving_average(time_series, window)
  assert result == [5.0, 4.67, 5.67, 6.67, 9.67, 28.67, 49.33, 72.67, 84.0, 93.33, 116.33]
  
  window = 0
  time_series = [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
  result = moving_average(time_series, window)
  assert result == [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
  
  window = 10
  time_series = [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
  result = moving_average(time_series, window)
  assert result == []
  
  window = 3
  time_series = []
  result = moving_average(time_series, window)
  assert result == []
output: def moving_average(data_points, window):
      moving_averages = []
      if window == 0:
          return data_points
      if window > len(data_points):
          return moving_averages
      for index in range(len(data_points)):
          if index + 1 >= window:
              window_data = data_points[index + 1 - window:index + 1]
              average = sum(window_data) / window
              moving_averages.append(average)
      return moving_averages