input: Write a function to compute the moving average of time-series data points. The function should take a list of data points and an integer window size as input and return a list of moving averages. If the window size is greater than the number of data points, return an empty list.
  
  def moving_average(data_points, window):
      if window > len(data_points):
          return []
      moving_averages = []
      for i in range(len(data_points) - window + 1):
          moving_averages.append(sum(data_points[i:i+window]) / window)
      return moving_averages
  
  assert moving_average([3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150], 3) == [5.0, 4.67, 5.67, 6.67, 9.67, 28.67, 49.33, 72.67, 84.0, 93.33, 116.33]
  assert moving_average([1, 2, 3, 4, 5], 2) == [1.5, 2.5, 3.5, 4.5]
  assert moving_average([1, 2, 3, 4, 5], 5) == [3.0]
  assert moving_average([1, 2, 3, 4, 5], 6) == []
  assert moving_average([], 3) == []
output: def moving_average(data_points, window):
      if window > len(data_points):
          return []
      moving_averages = []
      for i in range(len(data_points) - window + 1):
          moving_averages.append(sum(data_points[i:i+window]) / window)
      return moving_averages