profile
index
/usr/lib/python1.6/profile.py

Class for profiling Python code.

 
Modules
            
marshal
os
sys
time

 
Classes
            
Profile
HotProfile
OldProfile

 
class HotProfile(Profile)
      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 due to its very low overhead.
 
  
__init__(self, timer=None) from Profile
calibrate(self, m) from Profile
create_stats(self) from Profile
dump_stats(self, file) from Profile
get_time(self) from Profile
get_time_mac(self) from Profile
instrumented(self) from Profile
print_stats(self) from Profile
profiler_simulation(self, x, y, z) from Profile
run(self, cmd) from Profile
runcall(self, func, *args) from Profile
runctx(self, cmd, globals, locals) from Profile
set_cmd(self, cmd) from Profile
simple(self) from Profile
simulate_call(self, name) from Profile
simulate_cmd_complete(self) from Profile
snapshot_stats(self)
trace_dispatch(self, frame, event, arg) from Profile
trace_dispatch_call(self, frame, t)
trace_dispatch_exception(self, frame, t)
trace_dispatch_i(self, frame, event, arg) from Profile
trace_dispatch_l(self, frame, event, arg) from Profile
trace_dispatch_mac(self, frame, event, arg) from Profile
trace_dispatch_return(self, frame, t)

 
class OldProfile(Profile)
      A derived profiler that simulates the old style profile, providing
errant results on recursive functions. The reason for the usefulness of
this profiler is that it runs faster (i.e., less overhead).  It still
creates all the caller stats, and is quite useful when there is *no*
recursion in the user's code.
 
This code also shows how easy it is to create a modified profiler.
 
  
__init__(self, timer=None) from Profile
calibrate(self, m) from Profile
create_stats(self) from Profile
dump_stats(self, file) from Profile
get_time(self) from Profile
get_time_mac(self) from Profile
instrumented(self) from Profile
print_stats(self) from Profile
profiler_simulation(self, x, y, z) from Profile
run(self, cmd) from Profile
runcall(self, func, *args) from Profile
runctx(self, cmd, globals, locals) from Profile
set_cmd(self, cmd) from Profile
simple(self) from Profile
simulate_call(self, name) from Profile
simulate_cmd_complete(self) from Profile
snapshot_stats(self)
trace_dispatch(self, frame, event, arg) from Profile
trace_dispatch_call(self, frame, t)
trace_dispatch_exception(self, frame, t)
trace_dispatch_i(self, frame, event, arg) from Profile
trace_dispatch_l(self, frame, event, arg) from Profile
trace_dispatch_mac(self, frame, event, arg) from Profile
trace_dispatch_return(self, frame, t)

 
class Profile
      Profiler class.
 
self.cur is always a tuple.  Each such tuple corresponds to a stack
frame that is currently active (self.cur[-2]).  The following are the
definitions of its members.  We use this external "parallel stack" to
avoid contaminating the program that we are profiling. (old profiler
used to write into the frames local dictionary!!) Derived classes
can change the definition of some entries, as long as they leave
[-2:] intact.
 
[ 0] = Time that needs to be charged to the parent frame's function.
       It is used so that a function call will not have to access the
       timing data for the parent frame.
[ 1] = Total time spent in this frame's function, excluding time in
       subfunctions
[ 2] = Cumulative time spent in this frame's function, including time in
       all subfunctions to this frame.
[-3] = Name of the function that corresonds to this frame.  
[-2] = Actual frame that we correspond to (used to sync exception handling)
[-1] = Our parent 6-tuple (corresonds to frame.f_back)
 
Timing data for each function is stored as a 5-tuple in the dictionary
self.timings[].  The index is always the name stored in self.cur[4].
The following are the definitions of the members:
 
[0] = The number of times this function was called, not counting direct
      or indirect recursion,
[1] = Number of times this function appears on the stack, minus one
[2] = Total time spent internal to this function
[3] = Cumulative time that this function was present on the stack.  In
      non-recursive functions, this is the total execution time from start
      to finish of each invocation of a function, including time spent in
      all subfunctions.
[5] = A dictionary indicating for each function name, the number of times
      it was called by us.
 
  
__init__(self, timer=None)
calibrate(self, m)
create_stats(self)
dump_stats(self, file)
get_time(self)
get_time_mac(self)
instrumented(self)
# simulate a program with call/return event processing
print_stats(self)
profiler_simulation(self, x, y, z)
# simulate an event processing activity (from user's perspective)
run(self, cmd)
runcall(self, func, *args)
# This method is more useful to profile a single function call.
runctx(self, cmd, globals, locals)
set_cmd(self, cmd)
simple(self)
# simulate a program with no profiler activity
simulate_call(self, name)
simulate_cmd_complete(self)
snapshot_stats(self)
trace_dispatch(self, frame, event, arg)
trace_dispatch_call(self, frame, t)
trace_dispatch_exception(self, frame, t)
trace_dispatch_i(self, frame, event, arg)
trace_dispatch_l(self, frame, event, arg)
trace_dispatch_mac(self, frame, event, arg)
trace_dispatch_return(self, frame, t)

 
Functions
            
Stats(*args)
#****************************************************************************
help()
# print help
run(statement, *args)
# simplified user interface

 
Data
             __file__ = '/usr/lib/python1.6/profile.pyc'
__name__ = 'profile'