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+
+.. _profile:
+
+********************
+The Python Profilers
+********************
+
+.. sectionauthor:: James Roskind
+
+
+.. index:: single: InfoSeek Corporation
+
+Copyright © 1994, by InfoSeek Corporation, all rights reserved.
+
+Written by James Roskind. [#]_
+
+Permission to use, copy, modify, and distribute this Python software and its
+associated documentation for any purpose (subject to the restriction in the
+following sentence) without fee is hereby granted, provided that the above
+copyright notice appears in all copies, and that both that copyright notice and
+this permission notice appear in supporting documentation, and that the name of
+InfoSeek not be used in advertising or publicity pertaining to distribution of
+the software without specific, written prior permission.  This permission is
+explicitly restricted to the copying and modification of the software to remain
+in Python, compiled Python, or other languages (such as C) wherein the modified
+or derived code is exclusively imported into a Python module.
+
+INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
+INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT
+SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
+DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING
+OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+The profiler was written after only programming in Python for 3 weeks. As a
+result, it is probably clumsy code, but I don't know for sure yet 'cause I'm a
+beginner :-).  I did work hard to make the code run fast, so that profiling
+would be a reasonable thing to do.  I tried not to repeat code fragments, but
+I'm sure I did some stuff in really awkward ways at times.  Please send
+suggestions for improvements to: jar@netscape.com.  I won't promise *any*
+support.  ...but I'd appreciate the feedback.
+
+
+.. _profiler-introduction:
+
+Introduction to the profilers
+=============================
+
+.. index::
+   single: deterministic profiling
+   single: profiling, deterministic
+
+A :dfn:`profiler` is a program that describes the run time performance of a
+program, providing a variety of statistics.  This documentation describes the
+profiler functionality provided in the modules :mod:`profile` and :mod:`pstats`.
+This profiler provides :dfn:`deterministic profiling` of any Python programs.
+It also provides a series of report generation tools to allow users to rapidly
+examine the results of a profile operation.
+
+The Python standard library provides three different profilers:
+
+#. :mod:`profile`, a pure Python module, described in the sequel. Copyright ©
+   1994, by InfoSeek Corporation.
+
+   .. versionchanged:: 2.4
+      also reports the time spent in calls to built-in functions and methods.
+
+#. :mod:`cProfile`, a module written in C, with a reasonable overhead that makes
+   it suitable for profiling long-running programs. Based on :mod:`lsprof`,
+   contributed by Brett Rosen and Ted Czotter.
+
+   .. versionadded:: 2.5
+
+#. :mod:`hotshot`, a C module focusing on minimizing the overhead while
+   profiling, at the expense of long data post-processing times.
+
+   .. versionchanged:: 2.5
+      the results should be more meaningful than in the past: the timing core
+      contained a critical bug.
+
+The :mod:`profile` and :mod:`cProfile` modules export the same interface, so
+they are mostly interchangeables; :mod:`cProfile` has a much lower overhead but
+is not so far as well-tested and might not be available on all systems.
+:mod:`cProfile` is really a compatibility layer on top of the internal
+:mod:`_lsprof` module.  The :mod:`hotshot` module is reserved to specialized
+usages.
+
+.. % \section{How Is This Profiler Different From The Old Profiler?}
+.. % \nodename{Profiler Changes}
+.. % 
+.. % (This section is of historical importance only; the old profiler
+.. % discussed here was last seen in Python 1.1.)
+.. % 
+.. % The big changes from old profiling module are that you get more
+.. % information, and you pay less CPU time.  It's not a trade-off, it's a
+.. % trade-up.
+.. % 
+.. % To be specific:
+.. % 
+.. % \begin{description}
+.. % 
+.. % \item[Bugs removed:]
+.. % Local stack frame is no longer molested, execution time is now charged
+.. % to correct functions.
+.. % 
+.. % \item[Accuracy increased:]
+.. % Profiler execution time is no longer charged to user's code,
+.. % calibration for platform is supported, file reads are not done \emph{by}
+.. % profiler \emph{during} profiling (and charged to user's code!).
+.. % 
+.. % \item[Speed increased:]
+.. % Overhead CPU cost was reduced by more than a factor of two (perhaps a
+.. % factor of five), lightweight profiler module is all that must be
+.. % loaded, and the report generating module (\module{pstats}) is not needed
+.. % during profiling.
+.. % 
+.. % \item[Recursive functions support:]
+.. % Cumulative times in recursive functions are correctly calculated;
+.. % recursive entries are counted.
+.. % 
+.. % \item[Large growth in report generating UI:]
+.. % Distinct profiles runs can be added together forming a comprehensive
+.. % report; functions that import statistics take arbitrary lists of
+.. % files; sorting criteria is now based on keywords (instead of 4 integer
+.. % options); reports shows what functions were profiled as well as what
+.. % profile file was referenced; output format has been improved.
+.. % 
+.. % \end{description}
+
+
+.. _profile-instant:
+
+Instant User's Manual
+=====================
+
+This section is provided for users that "don't want to read the manual." It
+provides a very brief overview, and allows a user to rapidly perform profiling
+on an existing application.
+
+To profile an application with a main entry point of :func:`foo`, you would add
+the following to your module::
+
+   import cProfile
+   cProfile.run('foo()')
+
+(Use :mod:`profile` instead of :mod:`cProfile` if the latter is not available on
+your system.)
+
+The above action would cause :func:`foo` to be run, and a series of informative
+lines (the profile) to be printed.  The above approach is most useful when
+working with the interpreter.  If you would like to save the results of a
+profile into a file for later examination, you can supply a file name as the
+second argument to the :func:`run` function::
+
+   import cProfile
+   cProfile.run('foo()', 'fooprof')
+
+The file :file:`cProfile.py` can also be invoked as a script to profile another
+script.  For example::
+
+   python -m cProfile myscript.py
+
+:file:`cProfile.py` accepts two optional arguments on the command line::
+
+   cProfile.py [-o output_file] [-s sort_order]
+
+:option:`-s` only applies to standard output (:option:`-o` is not supplied).
+Look in the :class:`Stats` documentation for valid sort values.
+
+When you wish to review the profile, you should use the methods in the
+:mod:`pstats` module.  Typically you would load the statistics data as follows::
+
+   import pstats
+   p = pstats.Stats('fooprof')
+
+The class :class:`Stats` (the above code just created an instance of this class)
+has a variety of methods for manipulating and printing the data that was just
+read into ``p``.  When you ran :func:`cProfile.run` above, what was printed was
+the result of three method calls::
+
+   p.strip_dirs().sort_stats(-1).print_stats()
+
+The first method removed the extraneous path from all the module names. The
+second method sorted all the entries according to the standard module/line/name
+string that is printed. The third method printed out all the statistics.  You
+might try the following sort calls:
+
+.. % (this is to comply with the semantics of the old profiler).
+
+::
+
+   p.sort_stats('name')
+   p.print_stats()
+
+The first call will actually sort the list by function name, and the second call
+will print out the statistics.  The following are some interesting calls to
+experiment with::
+
+   p.sort_stats('cumulative').print_stats(10)
+
+This sorts the profile by cumulative time in a function, and then only prints
+the ten most significant lines.  If you want to understand what algorithms are
+taking time, the above line is what you would use.
+
+If you were looking to see what functions were looping a lot, and taking a lot
+of time, you would do::
+
+   p.sort_stats('time').print_stats(10)
+
+to sort according to time spent within each function, and then print the
+statistics for the top ten functions.
+
+You might also try::
+
+   p.sort_stats('file').print_stats('__init__')
+
+This will sort all the statistics by file name, and then print out statistics
+for only the class init methods (since they are spelled with ``__init__`` in
+them).  As one final example, you could try::
+
+   p.sort_stats('time', 'cum').print_stats(.5, 'init')
+
+This line sorts statistics with a primary key of time, and a secondary key of
+cumulative time, and then prints out some of the statistics. To be specific, the
+list is first culled down to 50% (re: ``.5``) of its original size, then only
+lines containing ``init`` are maintained, and that sub-sub-list is printed.
+
+If you wondered what functions called the above functions, you could now (``p``
+is still sorted according to the last criteria) do::
+
+   p.print_callers(.5, 'init')
+
+and you would get a list of callers for each of the listed functions.
+
+If you want more functionality, you're going to have to read the manual, or
+guess what the following functions do::
+
+   p.print_callees()
+   p.add('fooprof')
+
+Invoked as a script, the :mod:`pstats` module is a statistics browser for
+reading and examining profile dumps.  It has a simple line-oriented interface
+(implemented using :mod:`cmd`) and interactive help.
+
+
+.. _deterministic-profiling:
+
+What Is Deterministic Profiling?
+================================
+
+:dfn:`Deterministic profiling` is meant to reflect the fact that all *function
+call*, *function return*, and *exception* events are monitored, and precise
+timings are made for the intervals between these events (during which time the
+user's code is executing).  In contrast, :dfn:`statistical profiling` (which is
+not done by this module) randomly samples the effective instruction pointer, and
+deduces where time is being spent.  The latter technique traditionally involves
+less overhead (as the code does not need to be instrumented), but provides only
+relative indications of where time is being spent.
+
+In Python, since there is an interpreter active during execution, the presence
+of instrumented code is not required to do deterministic profiling.  Python
+automatically provides a :dfn:`hook` (optional callback) for each event.  In
+addition, the interpreted nature of Python tends to add so much overhead to
+execution, that deterministic profiling tends to only add small processing
+overhead in typical applications.  The result is that deterministic profiling is
+not that expensive, yet provides extensive run time statistics about the
+execution of a Python program.
+
+Call count statistics can be used to identify bugs in code (surprising counts),
+and to identify possible inline-expansion points (high call counts).  Internal
+time statistics can be used to identify "hot loops" that should be carefully
+optimized.  Cumulative time statistics should be used to identify high level
+errors in the selection of algorithms.  Note that the unusual handling of
+cumulative times in this profiler allows statistics for recursive
+implementations of algorithms to be directly compared to iterative
+implementations.
+
+
+Reference Manual -- :mod:`profile` and :mod:`cProfile`
+======================================================
+
+.. module:: cProfile
+   :synopsis: Python profiler
+
+
+The primary entry point for the profiler is the global function
+:func:`profile.run` (resp. :func:`cProfile.run`). It is typically used to create
+any profile information.  The reports are formatted and printed using methods of
+the class :class:`pstats.Stats`.  The following is a description of all of these
+standard entry points and functions.  For a more in-depth view of some of the
+code, consider reading the later section on Profiler Extensions, which includes
+discussion of how to derive "better" profilers from the classes presented, or
+reading the source code for these modules.
+
+
+.. function:: run(command[, filename])
+
+   This function takes a single argument that can be passed to the
+   :keyword:`exec` statement, and an optional file name.  In all cases this
+   routine attempts to :keyword:`exec` its first argument, and gather profiling
+   statistics from the execution. If no file name is present, then this function
+   automatically prints a simple profiling report, sorted by the standard name
+   string (file/line/function-name) that is presented in each line.  The
+   following is a typical output from such a call::
+
+            2706 function calls (2004 primitive calls) in 4.504 CPU seconds
+
+      Ordered by: standard name
+
+      ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+           2    0.006    0.003    0.953    0.477 pobject.py:75(save_objects)
+        43/3    0.533    0.012    0.749    0.250 pobject.py:99(evaluate)
+       ...
+
+   The first line indicates that 2706 calls were monitored.  Of those calls, 2004
+   were :dfn:`primitive`.  We define :dfn:`primitive` to mean that the call was not
+   induced via recursion. The next line: ``Ordered by: standard name``, indicates
+   that the text string in the far right column was used to sort the output. The
+   column headings include:
+
+   ncalls 
+      for the number of calls,
+
+   tottime 
+      for the total time spent in the given function (and excluding time made in calls
+      to sub-functions),
+
+   percall 
+      is the quotient of ``tottime`` divided by ``ncalls``
+
+   cumtime 
+      is the total time spent in this and all subfunctions (from invocation till
+      exit). This figure is accurate *even* for recursive functions.
+
+   percall 
+      is the quotient of ``cumtime`` divided by primitive calls
+
+   filename:lineno(function) 
+      provides the respective data of each function
+
+   When there are two numbers in the first column (for example, ``43/3``), then the
+   latter is the number of primitive calls, and the former is the actual number of
+   calls.  Note that when the function does not recurse, these two values are the
+   same, and only the single figure is printed.
+
+
+.. function:: runctx(command, globals, locals[, filename])
+
+   This function is similar to :func:`run`, with added arguments to supply the
+   globals and locals dictionaries for the *command* string.
+
+Analysis of the profiler data is done using the :class:`Stats` class.
+
+.. note::
+
+   The :class:`Stats` class is defined in the :mod:`pstats` module.
+
+
+.. module:: pstats
+   :synopsis: Statistics object for use with the profiler.
+
+
+.. class:: Stats(filename[, stream=sys.stdout[, ...]])
+
+   This class constructor creates an instance of a "statistics object" from a
+   *filename* (or set of filenames).  :class:`Stats` objects are manipulated by
+   methods, in order to print useful reports.  You may specify an alternate output
+   stream by giving the keyword argument, ``stream``.
+
+   The file selected by the above constructor must have been created by the
+   corresponding version of :mod:`profile` or :mod:`cProfile`.  To be specific,
+   there is *no* file compatibility guaranteed with future versions of this
+   profiler, and there is no compatibility with files produced by other profilers.
+   If several files are provided, all the statistics for identical functions will
+   be coalesced, so that an overall view of several processes can be considered in
+   a single report.  If additional files need to be combined with data in an
+   existing :class:`Stats` object, the :meth:`add` method can be used.
+
+   .. % (such as the old system profiler).
+
+   .. versionchanged:: 2.5
+      The *stream* parameter was added.
+
+
+.. _profile-stats:
+
+The :class:`Stats` Class
+------------------------
+
+:class:`Stats` objects have the following methods:
+
+
+.. method:: Stats.strip_dirs()
+
+   This method for the :class:`Stats` class removes all leading path information
+   from file names.  It is very useful in reducing the size of the printout to fit
+   within (close to) 80 columns.  This method modifies the object, and the stripped
+   information is lost.  After performing a strip operation, the object is
+   considered to have its entries in a "random" order, as it was just after object
+   initialization and loading.  If :meth:`strip_dirs` causes two function names to
+   be indistinguishable (they are on the same line of the same filename, and have
+   the same function name), then the statistics for these two entries are
+   accumulated into a single entry.
+
+
+.. method:: Stats.add(filename[, ...])
+
+   This method of the :class:`Stats` class accumulates additional profiling
+   information into the current profiling object.  Its arguments should refer to
+   filenames created by the corresponding version of :func:`profile.run` or
+   :func:`cProfile.run`. Statistics for identically named (re: file, line, name)
+   functions are automatically accumulated into single function statistics.
+
+
+.. method:: Stats.dump_stats(filename)
+
+   Save the data loaded into the :class:`Stats` object to a file named *filename*.
+   The file is created if it does not exist, and is overwritten if it already
+   exists.  This is equivalent to the method of the same name on the
+   :class:`profile.Profile` and :class:`cProfile.Profile` classes.
+
+   .. versionadded:: 2.3
+
+
+.. method:: Stats.sort_stats(key[, ...])
+
+   This method modifies the :class:`Stats` object by sorting it according to the
+   supplied criteria.  The argument is typically a string identifying the basis of
+   a sort (example: ``'time'`` or ``'name'``).
+
+   When more than one key is provided, then additional keys are used as secondary
+   criteria when there is equality in all keys selected before them.  For example,
+   ``sort_stats('name', 'file')`` will sort all the entries according to their
+   function name, and resolve all ties (identical function names) by sorting by
+   file name.
+
+   Abbreviations can be used for any key names, as long as the abbreviation is
+   unambiguous.  The following are the keys currently defined:
+
+   +------------------+----------------------+
+   | Valid Arg        | Meaning              |
+   +==================+======================+
+   | ``'calls'``      | call count           |
+   +------------------+----------------------+
+   | ``'cumulative'`` | cumulative time      |
+   +------------------+----------------------+
+   | ``'file'``       | file name            |
+   +------------------+----------------------+
+   | ``'module'``     | file name            |
+   +------------------+----------------------+
+   | ``'pcalls'``     | primitive call count |
+   +------------------+----------------------+
+   | ``'line'``       | line number          |
+   +------------------+----------------------+
+   | ``'name'``       | function name        |
+   +------------------+----------------------+
+   | ``'nfl'``        | name/file/line       |
+   +------------------+----------------------+
+   | ``'stdname'``    | standard name        |
+   +------------------+----------------------+
+   | ``'time'``       | internal time        |
+   +------------------+----------------------+
+
+   Note that all sorts on statistics are in descending order (placing most time
+   consuming items first), where as name, file, and line number searches are in
+   ascending order (alphabetical). The subtle distinction between ``'nfl'`` and
+   ``'stdname'`` is that the standard name is a sort of the name as printed, which
+   means that the embedded line numbers get compared in an odd way.  For example,
+   lines 3, 20, and 40 would (if the file names were the same) appear in the string
+   order 20, 3 and 40.  In contrast, ``'nfl'`` does a numeric compare of the line
+   numbers.  In fact, ``sort_stats('nfl')`` is the same as ``sort_stats('name',
+   'file', 'line')``.
+
+   For backward-compatibility reasons, the numeric arguments ``-1``, ``0``, ``1``,
+   and ``2`` are permitted.  They are interpreted as ``'stdname'``, ``'calls'``,
+   ``'time'``, and ``'cumulative'`` respectively.  If this old style format
+   (numeric) is used, only one sort key (the numeric key) will be used, and
+   additional arguments will be silently ignored.
+
+   .. % For compatibility with the old profiler,
+
+
+.. method:: Stats.reverse_order()
+
+   This method for the :class:`Stats` class reverses the ordering of the basic list
+   within the object.  Note that by default ascending vs descending order is
+   properly selected based on the sort key of choice.
+
+   .. % This method is provided primarily for
+   .. % compatibility with the old profiler.
+
+
+.. method:: Stats.print_stats([restriction, ...])
+
+   This method for the :class:`Stats` class prints out a report as described in the
+   :func:`profile.run` definition.
+
+   The order of the printing is based on the last :meth:`sort_stats` operation done
+   on the object (subject to caveats in :meth:`add` and :meth:`strip_dirs`).
+
+   The arguments provided (if any) can be used to limit the list down to the
+   significant entries.  Initially, the list is taken to be the complete set of
+   profiled functions.  Each restriction is either an integer (to select a count of
+   lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a
+   percentage of lines), or a regular expression (to pattern match the standard
+   name that is printed; as of Python 1.5b1, this uses the Perl-style regular
+   expression syntax defined by the :mod:`re` module).  If several restrictions are
+   provided, then they are applied sequentially.  For example::
+
+      print_stats(.1, 'foo:')
+
+   would first limit the printing to first 10% of list, and then only print
+   functions that were part of filename :file:`.\*foo:`.  In contrast, the
+   command::
+
+      print_stats('foo:', .1)
+
+   would limit the list to all functions having file names :file:`.\*foo:`, and
+   then proceed to only print the first 10% of them.
+
+
+.. method:: Stats.print_callers([restriction, ...])
+
+   This method for the :class:`Stats` class prints a list of all functions that
+   called each function in the profiled database.  The ordering is identical to
+   that provided by :meth:`print_stats`, and the definition of the restricting
+   argument is also identical.  Each caller is reported on its own line.  The
+   format differs slightly depending on the profiler that produced the stats:
+
+   * With :mod:`profile`, a number is shown in parentheses after each caller to
+     show how many times this specific call was made.  For convenience, a second
+     non-parenthesized number repeats the cumulative time spent in the function
+     at the right.
+
+   * With :mod:`cProfile`, each caller is preceeded by three numbers: the number of
+     times this specific call was made, and the total and cumulative times spent in
+     the current function while it was invoked by this specific caller.
+
+
+.. method:: Stats.print_callees([restriction, ...])
+
+   This method for the :class:`Stats` class prints a list of all function that were
+   called by the indicated function.  Aside from this reversal of direction of
+   calls (re: called vs was called by), the arguments and ordering are identical to
+   the :meth:`print_callers` method.
+
+
+.. _profile-limits:
+
+Limitations
+===========
+
+One limitation has to do with accuracy of timing information. There is a
+fundamental problem with deterministic profilers involving accuracy.  The most
+obvious restriction is that the underlying "clock" is only ticking at a rate
+(typically) of about .001 seconds.  Hence no measurements will be more accurate
+than the underlying clock.  If enough measurements are taken, then the "error"
+will tend to average out. Unfortunately, removing this first error induces a
+second source of error.
+
+The second problem is that it "takes a while" from when an event is dispatched
+until the profiler's call to get the time actually *gets* the state of the
+clock.  Similarly, there is a certain lag when exiting the profiler event
+handler from the time that the clock's value was obtained (and then squirreled
+away), until the user's code is once again executing.  As a result, functions
+that are called many times, or call many functions, will typically accumulate
+this error. The error that accumulates in this fashion is typically less than
+the accuracy of the clock (less than one clock tick), but it *can* accumulate
+and become very significant.
+
+The problem is more important with :mod:`profile` than with the lower-overhead
+:mod:`cProfile`.  For this reason, :mod:`profile` provides a means of
+calibrating itself for a given platform so that this error can be
+probabilistically (on the average) removed. After the profiler is calibrated, it
+will be more accurate (in a least square sense), but it will sometimes produce
+negative numbers (when call counts are exceptionally low, and the gods of
+probability work against you :-). )  Do *not* be alarmed by negative numbers in
+the profile.  They should *only* appear if you have calibrated your profiler,
+and the results are actually better than without calibration.
+
+
+.. _profile-calibration:
+
+Calibration
+===========
+
+The profiler of the :mod:`profile` module subtracts a constant from each event
+handling time to compensate for the overhead of calling the time function, and
+socking away the results.  By default, the constant is 0. The following
+procedure can be used to obtain a better constant for a given platform (see
+discussion in section Limitations above). ::
+
+   import profile
+   pr = profile.Profile()
+   for i in range(5):
+       print pr.calibrate(10000)
+
+The method executes the number of Python calls given by the argument, directly
+and again under the profiler, measuring the time for both. It then computes the
+hidden overhead per profiler event, and returns that as a float.  For example,
+on an 800 MHz Pentium running Windows 2000, and using Python's time.clock() as
+the timer, the magical number is about 12.5e-6.
+
+The object of this exercise is to get a fairly consistent result. If your
+computer is *very* fast, or your timer function has poor resolution, you might
+have to pass 100000, or even 1000000, to get consistent results.
+
+When you have a consistent answer, there are three ways you can use it: [#]_ ::
+
+   import profile
+
+   # 1. Apply computed bias to all Profile instances created hereafter.
+   profile.Profile.bias = your_computed_bias
+
+   # 2. Apply computed bias to a specific Profile instance.
+   pr = profile.Profile()
+   pr.bias = your_computed_bias
+
+   # 3. Specify computed bias in instance constructor.
+   pr = profile.Profile(bias=your_computed_bias)
+
+If you have a choice, you are better off choosing a smaller constant, and then
+your results will "less often" show up as negative in profile statistics.
+
+
+.. _profiler-extensions:
+
+Extensions --- Deriving Better Profilers
+========================================
+
+The :class:`Profile` class of both modules, :mod:`profile` and :mod:`cProfile`,
+were written so that derived classes could be developed to extend the profiler.
+The details are not described here, as doing this successfully requires an
+expert understanding of how the :class:`Profile` class works internally.  Study
+the source code of the module carefully if you want to pursue this.
+
+If all you want to do is change how current time is determined (for example, to
+force use of wall-clock time or elapsed process time), pass the timing function
+you want to the :class:`Profile` class constructor::
+
+   pr = profile.Profile(your_time_func)
+
+The resulting profiler will then call :func:`your_time_func`.
+
+:class:`profile.Profile`
+   :func:`your_time_func` should return a single number, or a list of numbers whose
+   sum is the current time (like what :func:`os.times` returns).  If the function
+   returns a single time number, or the list of returned numbers has length 2, then
+   you will get an especially fast version of the dispatch routine.
+
+   Be warned that you should calibrate the profiler class for the timer function
+   that you choose.  For most machines, a timer that returns a lone integer value
+   will provide the best results in terms of low overhead during profiling.
+   (:func:`os.times` is *pretty* bad, as it returns a tuple of floating point
+   values).  If you want to substitute a better timer in the cleanest fashion,
+   derive a class and hardwire a replacement dispatch method that best handles your
+   timer call, along with the appropriate calibration constant.
+
+:class:`cProfile.Profile`
+   :func:`your_time_func` should return a single number.  If it returns plain
+   integers, you can also invoke the class constructor with a second argument
+   specifying the real duration of one unit of time.  For example, if
+   :func:`your_integer_time_func` returns times measured in thousands of seconds,
+   you would constuct the :class:`Profile` instance as follows::
+
+      pr = profile.Profile(your_integer_time_func, 0.001)
+
+   As the :mod:`cProfile.Profile` class cannot be calibrated, custom timer
+   functions should be used with care and should be as fast as possible.  For the
+   best results with a custom timer, it might be necessary to hard-code it in the C
+   source of the internal :mod:`_lsprof` module.
+
+.. rubric:: Footnotes
+
+.. [#] Updated and converted to LaTeX by Guido van Rossum. Further updated by Armin
+   Rigo to integrate the documentation for the new :mod:`cProfile` module of Python
+   2.5.
+
+.. [#] Prior to Python 2.2, it was necessary to edit the profiler source code to embed
+   the bias as a literal number.  You still can, but that method is no longer
+   described, because no longer needed.
+