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It is the only way I could work out so far that can
"predict" how much a function will be accelerated when run
under Psyco.  This is a very precious indication for an
automatic Psyco-binder.  The following table shows the
results with the various test functions of the
distribution's "test.py" file:

(fn name)     (speed-up)  (bytecode insns per second)
f1              106.00         
2310545                        
f4              11.33          
2819100                        
f5              12.08          
2992445                        
f6              1.35           
412022                         
f7              2.24           
1331353                        
f7bis           10.29          
1632296                        

The third column is '(tick_counter * check_interval) /
execution_time'.  The correlation between the two columns is
admittedly not perfect, but still we can see that it was not
worthy to try and accelerate f6 because it didn't spend a
lot of time actually interpreting bytecodes.

Note that similar information could be obtained by setting a
line-tracing hook, counting not instructions but lines
(which is less precise but still a good approximation). 
However, line tracing is *much* too slow for anything but
debugging usage.