I would like to write the profilehooks output to a .prof file. In order to convert the file to a qcachgrind file and visualize the profiling results. (I can't use cProfile as cProfile, does not profile the code)
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                    I assume you are doing this to speed up the code. You understand that stack samples expose speedups [*without needing to measure them.*](http://stackoverflow.com/a/4299378/23771) They find at least as much as profilers (deterministic or statistical). – Mike Dunlavey Sep 26 '15 at 16:19
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        Its not on the web, but the documentation says:
profile(fn=None, skip=0, filename=None, immediate=False, dirs=False, sort=None, entries=40, profiler=('cProfile', 'profile', 'hotshot'), stdout=True)
    Mark `fn` for profiling.
    If `skip` is > 0, first `skip` calls to `fn` will not be profiled.
    If `immediate` is False, profiling results will be printed to
    sys.stdout on program termination.  Otherwise results will be printed
    after each call.  (If you don't want this, set stdout=False and specify a
    `filename` to store profile data.)
    If `dirs` is False only the name of the file will be printed.
    Otherwise the full path is used.
    `sort` can be a list of sort keys (defaulting to ['cumulative',
    'time', 'calls']).  The following ones are recognized::
        'calls'      -- call count
        'cumulative' -- cumulative time
        'file'       -- file name
        'line'       -- line number
        'module'     -- file name
        'name'       -- function name
        'nfl'        -- name/file/line
        'pcalls'     -- call count
        'stdname'    -- standard name
        'time'       -- internal time
    `entries` limits the output to the first N entries.
    `profiler` can be used to select the preferred profiler, or specify a
    sequence of them, in order of preference.  The default is ('cProfile'.
    'profile', 'hotshot').
    If `filename` is specified, the profile stats will be stored in the
    named file.  You can load them with pstats.Stats(filename) or use a
    visualization tool like RunSnakeRun.
    Usage::
        def fn(...):
            ...
        fn = profile(fn, skip=1)
    If you are using Python 2.4, you should be able to use the decorator
    syntax::
        @profile(skip=3)
        def fn(...):
            ...
    or just ::
        @profile
        def fn(...):
            ...
 
    
    
        Davoud Taghawi-Nejad
        
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