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)

This discussion inspired me to refine Scalene's output further. Scalene now separates out CPU time spent in Python from time spent in C (that is, not executing in the interpreter). I think this is the best of both worlds: all CPU time is accounted for, and it's easy for users to see what code is running where. Thanks!

  sample-sample.py: % of CPU time =  99.58% out of   3.34s.
           | CPU %    | CPU %    | 
    Line   | (Python) | (C)      | [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   |          |          |         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   |   6.61%  |   0.41%  |     for i in range(n):
      17   |  35.17%  |   3.97%  |         s += i
      18   |          |          |     return s
      19   |          |          | 
      20   |          |          | @timed
      21   |          |          | def f2(n):
      22   |   0.30%  |  53.53%  |     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)

I like it, and I appreciate that you changed your mind - I'm still staunchly in the flamgraph camp of capturing and displaying whole stacks, but now I could see using this for small code samples, especially with the memory usage information added on top.

Thanks for being good with my grumpy feedback!

Just wanted to take a moment to appreciate the awesome response to feedback and new added feature.

Yeah, love this kind of discussion and interaction on hnews!

Yeah this is some of the best HN back and forth I’ve seen!

Kudos to everyone involved.