On Tue, 23 Mar 2010 12:16:03 pm Jojo Mwebaze wrote:
> Hello Tutor
>
> I have two problems, any help will be highly appreciated.
>
> How is possible to trace the all method calls, object instantiations,
> variables used in running an experiment dynamically, without putting
> print - or log statements in my code? - some sort of provenance!
Look at the profile module for starters.
>>> def test(x):
... y = -42
... for i in xrange(10000):
... y += func(i)
... return x + y
...
>>> def func(x):
... return x
...
>>>
>>> import profile
>>> profile.run("test(3)")
10004 function calls in 0.286 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.002 0.002 0.002 0.002 :0(setprofile)
10000 0.145 0.000 0.145 0.000 <stdin>:1(func)
1 0.139 0.139 0.284 0.284 <stdin>:1(test)
1 0.000 0.000 0.284 0.284 <string>:1(<module>)
0 0.000 0.000 profile:0(profiler)
1 0.000 0.000 0.286 0.286 profile:0(test(3))
> I would like to create a tool that can look into pyc files to find
> classes/methods that was executed without looking the the source
> code. Is this possible in python.
Look at the dis module.
>>> import dis
>>> dis.dis(test)
2 0 LOAD_CONST 1 (-42)
3 STORE_FAST 1 (y)
3 6 SETUP_LOOP 36 (to 45)
9 LOAD_GLOBAL 0 (xrange)
12 LOAD_CONST 2 (10000)
15 CALL_FUNCTION 1
18 GET_ITER
>> 19 FOR_ITER 22 (to 44)
22 STORE_FAST 2 (i)
4 25 LOAD_FAST 1 (y)
28 LOAD_GLOBAL 1 (func)
31 LOAD_FAST 2 (i)
34 CALL_FUNCTION 1
37 INPLACE_ADD
38 STORE_FAST 1 (y)
41 JUMP_ABSOLUTE 19
>> 44 POP_BLOCK
5 >> 45 LOAD_FAST 0 (x)
48 LOAD_FAST 1 (y)
51 BINARY_ADD
52 RETURN_VALUE
--
Steven D'Aprano
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