Mostly I just print to stdout, I imagine more flexibility would be needed in general.
This is for python 2.7 - don't know if it works for 3.
def profile(sort='time', restriction=(), callers=None, callees=None, filename=None): def _profileDecorator(func): "print profile stats for decorated function" def wrapper(*args, **kwargs): print 'Profile for:', func.__name__
prof = cProfile.Profile() result = prof.runcall(func, *args, **kwargs) _, statsFileName = tempfile.mkstemp() prof.dump_stats(statsFileName) if filename is None: stats = pstats.Stats(statsFileName) else: stats = pstats.Stats(statsFileName, stream=open(filename, 'w')) if isinstance(sort, basestring): stats.sort_stats(sort) else: stats.sort_stats(*sort) if isinstance(restriction, (tuple, list)): stats.print_stats(*restriction) else: stats.print_stats(restriction) if callers is not None: if isinstance(callers, basestring): stats.print_callers(callers) else: stats.print_callers(*callers) if callees is not None: if isinstance(callees, basestring): stats.print_callees(callees) else: stats.print_callees(*callees) return result return wrapper return _profileDecorator
On 3 November 2016 at 09:58, Ben Hoyt firstname.lastname@example.org wrote:
Okay, got it, that sounds fair enough. With your @profile decorator how do you tell it when and where to print the output? Can you post the source for your decorator?
On Wed, Nov 2, 2016 at 4:52 PM, Tim Mitchell email@example.com wrote:
I use an @profile() decorator for almost all my profiling. If you want to profile function foo you just decorate it and re-run the program. With a with block you have to find the places where foo is called and put with statements around the calls. I think both approaches are equally valid and useful.