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Python Library Reference |  |
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Python Library Reference | |
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This profiler is the fastest derived profile example. It does not calculate caller-callee relationships, and does not calculate cumulative time under a function. It only calculates time spent in a function, so it runs very quickly (re: very low overhead). In truth, the basic profiler is so fast, that is probably not worth the savings to give up the data, but this class still provides a nice example.
class HotProfile(Profile):
def trace_dispatch_exception(self, frame, t):
rt, rtt, rfn, rframe, rcur = self.cur
if rcur and not rframe is frame:
return self.trace_dispatch_return(rframe, t)
return 0
def trace_dispatch_call(self, frame, t):
self.cur = (t, 0, frame, self.cur)
return 1
def trace_dispatch_return(self, frame, t):
rt, rtt, frame, rcur = self.cur
rfn = `frame.f_code`
pt, ptt, pframe, pcur = rcur
self.cur = pt, ptt+rt, pframe, pcur
if self.timings.has_key(rfn):
nc, tt = self.timings[rfn]
self.timings[rfn] = nc + 1, rt + rtt + tt
else:
self.timings[rfn] = 1, rt + rtt
return 1
def snapshot_stats(self):
self.stats = {}
for func in self.timings.keys():
nc, tt = self.timings[func]
nor_func = self.func_normalize(func)
self.stats[nor_func] = nc, nc, tt, 0, {}
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Python Library Reference | |
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