Re: being impressed by the memory profiling hacks, thanks! :)

And you persuaded me. New version to be uploaded momentarily that attributes time to line 22.

  % python3 -m scalene sample-sample.py                                        
  f1 1.8081 312499987500000
  f2 1.7910 2812499962500000
  f3 1.5034 1.5
  sample-sample.py: % of CPU time =  70.53% out of   5.10s.
    Line  | CPU %    |  [sample-sample.py]
       1  |          | #!/usr/bin/env python
       2  |          |  
       3  |          | import time
       4  |          |      
       5  |          | def timed(f):
       6  |          |     def f_timed(*args, **kwargs):
       7  |          |         t = time.time()
       8  |          |         r = f(*args, **kwargs)
       9  |   0.32%  |         t = time.time() - t
      10  |          |         print('%s %.4f %s' % (f.__name__, t, r))
      11  |          |     return f_timed
      12  |          | 
      13  |          | @timed
      14  |          | def f1(n):
      15  |          |     s = 0
      16  |   8.58%  |     for i in range(n):
      17  |  41.34%  |         s += i
      18  |          |     return s
      19  |          | 
      20  |          | @timed
      21  |          | def f2(n):
      22  |  49.76%  |     return sum(range(n))
      23  |          | 
      24  |          | @timed
      25  |          | def f3(t):
      26  |          |     time.sleep(t)
      27  |          |     return t
      28  |          | 
      29  |          | if __name__ == '__main__':
      30  |          |     f1(25000000)
      31  |          |     f2(75000000)
      32  |          |     f3(1.5)