line_profiler is a module for doing line-by-line profiling of functions. kernprof is a convenient script for running either line_profiler or the standard library's cProfile module. Download: http://pypi.python.org/pypi/line_profiler Docs: http://packages.python.org/line_profiler HG Repo: http://www.enthought.com/~rkern/cgi-bin/hgwebdir.cgi/line_profiler/ Just kernprof: http://packages.python.org/line_profiler/kernprof.py line_profiler ------------- line_profiler will time individual statements in specified functions. For example, here are the results of profiling a single function from a decorated version of the pystone.py benchmark (the first two lines are output from pystone.py):: Pystone(1.1) time for 50000 passes = 2.48 This machine benchmarks at 20161.3 pystones/second Wrote profile results to pystone.py.lprof Timer unit: 1e-06 s File: pystone.py Function: Proc2 at line 149 Total time: 0.606656 s Line # Hits Time Per Hit % Time Line Contents ============================================================== 149 @profile 150 def Proc2(IntParIO): 151 50000 82003 1.6 13.5 IntLoc = IntParIO + 10 152 50000 63162 1.3 10.4 while 1: 153 50000 69065 1.4 11.4 if Char1Glob == 'A': 154 50000 66354 1.3 10.9 IntLoc = IntLoc - 1 155 50000 67263 1.3 11.1 IntParIO = IntLoc - IntGlob 156 50000 65494 1.3 10.8 EnumLoc = Ident1 157 50000 68001 1.4 11.2 if EnumLoc == Ident1: 158 50000 63739 1.3 10.5 break 159 50000 61575 1.2 10.1 return IntParIO kernprof -------- kernprof will run your scripts under one of cProfiler or line_profiler in a variety of convenient ways. It has a few main features: * Encapsulation of profiling concerns. You do not have to modify your script in order to initiate profiling and save the results. * Robust script execution. Many scripts require things like __name__, __file__, and sys.path to be set relative to it. A naive approach at encapsulation would just use execfile(), but many scripts which rely on that information will fail. kernprof will set those variables correctly before executing the script. * Easy executable location. If you are profiling an application installed on your PATH, you can just give the name of the executable. If kernprof does not find the given script in the current directory, it will search your PATH for it. * Inserting the profiler into __builtins__. Sometimes, you just want to profile a small part of your code. With the [-b/--builtin] argument, the Profiler will be instantiated and inserted into your __builtins__ with the name "profile". It may be used as a decorator on the targeted functions. * Pre-profiling setup. With the [-s/--setup] option, you can provide a script which will be executed without profiling before executing the main script. This is typically useful for cases where imports of large libraries like wxPython or VTK are interfering with your results. The results of profile script_to_profile.py will be written to script_to_profile.py.prof by default. It will be a typical marshalled file that can be read with pstats.Stats(). They may be interactively viewed with the command:: $ python -m pstats script_to_profile.py.prof Such files may also be viewed with graphical tools like kcachegrind_ through the converter program pyprof2calltree_ or RunSnakeRun_. .. _kcachegrind: http://kcachegrind.sourceforge.net/html/Home.html .. _pyprof2calltree: http://pypi.python.org/pypi/pyprof2calltree/ .. _RunSnakeRun: http://www.vrplumber.com/programming/runsnakerun/ -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco