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

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)