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Lots of small markup adjustments.

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Fred Drake 2003-04-09 04:06:37 +00:00
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58
Doc/lib/libhotshot.tex
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@ -2,31 +2,31 @@
High performance logging profiler}
\declaremodule{standard}{hotshot}
\modulesynopsis{High performance logging profiler, mostly written in C.}
\moduleauthor{Fred L. Drake, Jr.}{fdrake@acm.org}
\sectionauthor{Anthony Baxter}{anthony@interlink.com.au}
\versionadded{2.2}
\modulesynopsis{High performance logging profiler, mostly written in C.}
This module provides a nicer interface to the \code{_hotshot} C module.
This module provides a nicer interface to the \module{_hotshot} C module.
Hotshot is a replacement for the existing \refmodule{profile} module. As it's
written mostly in C, it should result in a much smaller performance impact
than the existing profile module.
written mostly in C, it should result in a much smaller performance impact
than the existing \refmodule{profile} module.
\begin{classdesc}{Profile}{logfile, \optional{, lineevents=0, linetimings=1}}
The profiler object. The argument \var{logfile} is the name of a log file
to use for logged profile data. The argument \var{lineevents} specifies whether
to generate events for every source line, or just on function call/return. It
defaults to 0 (only log function call/return). The argument \var{linetimings}
specifies whether to record timing information. It defaults to 1 (store timing
\begin{classdesc}{Profile}{logfile\optional{,
lineevents\code{=0}\optional{,
linetimings\code{=1}}}}
The profiler object. The argument \var{logfile} is the name of a log
file to use for logged profile data. The argument \var{lineevents}
specifies whether to generate events for every source line, or just on
function call/return. It defaults to \code{0} (only log function
call/return). The argument \var{linetimings} specifies whether to
record timing information. It defaults to \code{1} (store timing
information).
\end{classdesc}
\subsection{Profile Objects \label{hotshot-objects}}
Profile objects have the following methods:
@ -38,22 +38,19 @@ Add an arbitrary labelled value to the profile output.
\begin{methoddesc}{close}{}
Close the logfile and terminate the profiler.
\end{methoddesc}
%
\begin{methoddesc}{fileno}{}
Return the file descriptor of the profiler's log file.
\end{methoddesc}
\begin{methoddesc}{run}{cmd}
Profile an exec-compatible string in the script environment.
The globals from the \module{__main__} module are used as
Profile an \keyword{exec}-compatible string in the script environment.
The globals from the \refmodule[main]{__main__} module are used as
both the globals and locals for the script.
\end{methoddesc}
\begin{methoddesc}{runcall}{func, *args, **keywords}
Profile a single call of a callable.
Additional positional and keyword arguments may be passed
along; the result of the call is returned, and exceptions are
allowed to propogate cleanly, while ensuring that profiling is
@ -62,8 +59,7 @@ disabled on the way out.
\begin{methoddesc}{runctx}{cmd, globals, locals}
Evaluate an exec-compatible string in a specific environment.
Evaluate an \keyword{exec}-compatible string in a specific environment.
The string is compiled before profiling begins.
\end{methoddesc}
@ -75,32 +71,33 @@ Start the profiler.
Stop the profiler.
\end{methoddesc}
\subsection{Using hotshot data}
\declaremodule{standard}{hotshot.stats}
\subsection{Using hotshot data}
\declaremodule{standard}{hotshot.stats}
\modulesynopsis{Statistical analysis for Hotshot}
\versionadded{2.2}
This module loads hotshot profiling data into the standard \module{pstats}
This module loads hotshot profiling data into the standard \module{pstats}
Stats objects.
\begin{funcdesc}{load}{filename}
Load hotshot data from \var{filename}. Returns an instance
Load hotshot data from \var{filename}. Returns an instance
of the \class{pstats.Stats} class.
\end{funcdesc}
\begin{seealso}
\seemodule{profile}{The profile module's \class{Stats} class }
\seemodule{profile}{The \module{profile} module's \class{Stats} class}
\end{seealso}
\subsection{Example Usage \label{hotshot-example}}
Note that this example runs the python "benchmark" pystones. It can
Note that this example runs the python ``benchmark'' pystones. It can
take some time to run, and will produce large output files.
\begin{verbatim}
>>> import hotshot, hotshot.stats, test.pystone
>>> prof = hotshot.Profile("stones.prof")
>>> benchtime, stones = prof.runcall(test.pystone.pystones)
@ -120,7 +117,4 @@ take some time to run, and will produce large output files.
.
.
.
\end{verbatim}

165
Doc/lib/libtimeit.tex
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@ -4,36 +4,36 @@
\declaremodule{standard}{timeit}
\modulesynopsis{Measure the execution time of small code snippets.}
\versionadded{2.3}
\index{Benchmarking}
\index{Performance}
\versionadded{2.3}
This module provides a simple way to time small bits of Python code.
It has both command line as well as callable interfaces. It avoids a
number of common traps for measuring execution times. See also Tim
Peters' introduction to the ``Algorithms'' chapter in the
\citetitle{Python Cookbook}, published by O'Reilly.
This module provides a simple way to time small bits of Python code. It has
both command line as well as callable interfaces. It avoids a number of
common traps for measuring execution times. See also Tim Peters'
introduction to the Algorithms chapter in the ``Python Cookbook'', published
by O'Reilly.
The module defines the following public class:
The module interface defines the following public class:
\begin{classdesc}{Timer}{\optional{stmt='pass'
\optional{, setup='pass'
\optional{, timer=<timer function>}}}}
\begin{classdesc}{Timer}{\optional{stmt=\code{'pass'}
\optional{, setup=\code{'pass'}
\optional{, timer=<timer function>}}}}
Class for timing execution speed of small code snippets.
The constructor takes a statement to be timed, an additional statement used
for setup, and a timer function. Both statements default to 'pass'; the
timer function is platform-dependent (see the module doc string).
The constructor takes a statement to be timed, an additional statement
used for setup, and a timer function. Both statements default to
\code{'pass'}; the timer function is platform-dependent (see the
module doc string). The statements may contain newlines, as long as
they don't contain multi-line string literals.
To measure the execution time of the first statement, use the timeit()
method. The repeat() method is a convenience to call timeit() multiple
times and return a list of results.
To measure the execution time of the first statement, use the
\method{timeit()} method. The \method{repeat()} method is a
convenience to call \method{timeit()} multiple times and return a list
of results.
\end{classdesc}
The statements may contain newlines, as long as they don't contain
multi-line string literals.
\begin{methoddesc}{print_exc}{\optional{file=None}}
\begin{methoddesc}{print_exc}{\optional{file=\constant{None}}}
Helper to print a traceback from the timed code.
Typical use:
@ -48,20 +48,21 @@ Typical use:
The advantage over the standard traceback is that source lines in the
compiled template will be displayed.
The optional file argument directs where the traceback is sent; it defaults
to \code{sys.stderr}.
The optional \var{file} argument directs where the traceback is sent;
it defaults to \code{sys.stderr}.
\end{methoddesc}
\begin{methoddesc}{repeat}{\optional{repeat=3\optional{, number=1000000}}}
\begin{methoddesc}{repeat}{\optional{repeat\code{=3}\optional{,
number\code{=1000000}}}}
Call \method{timeit()} a few times.
This is a convenience function that calls the \method{timeit()} repeatedly,
returning a list of results. The first argument specifies how many times to
call \function{timeit()}. The second argument specifies the \code{number}
argument for \function{timeit()}.
This is a convenience function that calls the \method{timeit()}
repeatedly, returning a list of results. The first argument specifies
how many times to call \method{timeit()}. The second argument
specifies the \var{number} argument for \function{timeit()}.
Note: it's tempting to calculate mean and standard deviation from the result
\begin{notice}
It's tempting to calculate mean and standard deviation from the result
vector and report these. However, this is not very useful. In a typical
case, the lowest value gives a lower bound for how fast your machine can run
the given code snippet; higher values in the result vector are typically not
@ -70,25 +71,26 @@ with your timing accuracy. So the \function{min()} of the result is
probably the only number you should be interested in. After that, you
should look at the entire vector and apply common sense rather than
statistics.
\end{notice}
\end{methoddesc}
\begin{methoddesc}{timeit}{\optional{number=1000000}}
Time \code{number} executions of the main statement.
To be precise, this executes the setup statement once, and then returns the
time it takes to execute the main statement a number of times, as a float
measured in seconds. The argument is the number of times through the loop,
defaulting to one million. The main statement, the setup statement and the
timer function to be used are passed to the constructor.
\begin{methoddesc}{timeit}{\optional{number\code{=1000000}}}
Time \var{number} executions of the main statement.
This executes the setup statement once, and then
returns the time it takes to execute the main statement a number of
times, measured in seconds as a float. The argument is the number of
times through the loop, defaulting to one million. The main
statement, the setup statement and the timer function to be used are
passed to the constructor.
\end{methoddesc}
\end{classdesc}
\subsection{Command Line Interface}
When called as a program from the command line, the following form is used:
\begin{verbatim}
python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement ...]
python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement ...]
\end{verbatim}
where the following options are understood:
@ -97,55 +99,64 @@ where the following options are understood:
\item[-n N/--number=N] how many times to execute 'statement'
\item[-r N/--repeat=N] how many times to repeat the timer (default 3)
\item[-s S/--setup=S] statement to be executed once initially (default
'pass')
\item[-t/--time] use time.time() (default on all platforms but Windows)
\item[-c/--clock] use time.clock() (default on Windows)
\code{'pass'})
\item[-t/--time] use \function{time.time()}
(default on all platforms but Windows)
\item[-c/--clock] use \function{time.clock()} (default on Windows)
\item[-v/--verbose] print raw timing results; repeat for more digits
precision
precision
\item[-h/--help] print a short usage message and exit
\end{description}
A multi-line statement may be given by specifying each line as a separate
statement argument; indented lines are possible by enclosing an argument in
quotes and using leading spaces. Multiple -s options are treated similarly.
A multi-line statement may be given by specifying each line as a
separate statement argument; indented lines are possible by enclosing
an argument in quotes and using leading spaces. Multiple
\programopt{-s} options are treated similarly.
If -n is not given, a suitable number of loops is calculated by trying
successive powers of 10 until the total time is at least 0.2 seconds.
If \programopt{-n} is not given, a suitable number of loops is
calculated by trying successive powers of 10 until the total time is
at least 0.2 seconds.
The default timer function is platform dependent. On Windows, clock() has
microsecond granularity but time()'s granularity is 1/60th of a second; on
Unix, clock() has 1/100th of a second granularity and time() is much more
precise. On either platform, the default timer functions measures wall
clock time, not the CPU time. This means that other processes running on
the same computer may interfere with the timing. The best thing to do when
accurate timing is necessary is to repeat the timing a few times and use the
best time. The -r option is good for this; the default of 3 repetitions is
probably enough in most cases. On Unix, you can use clock() to measure CPU
time.
The default timer function is platform dependent. On Windows,
\function{time.clock()} has microsecond granularity but
\function{time.time()}'s granularity is 1/60th of a second; on \UNIX,
\function{time.clock()} has 1/100th of a second granularity and
\function{time.time()} is much more precise. On either platform, the
default timer functions measures wall clock time, not the CPU time.
This means that other processes running on the same computer may
interfere with the timing. The best thing to do when accurate timing
is necessary is to repeat the timing a few times and use the best
time. The \programopt{-r} option is good for this; the default of 3
repetitions is probably enough in most cases. On \UNIX, you can use
\function{time.clock()} to measure CPU time.
Note: there is a certain baseline overhead associated with executing a pass
statement. The code here doesn't try to hide it, but you should be aware of
it. The baseline overhead can be measured by invoking the program without
arguments.
\begin{notice}
There is a certain baseline overhead associated with executing a
pass statement. The code here doesn't try to hide it, but you
should be aware of it. The baseline overhead can be measured by
invoking the program without arguments.
\end{notice}
The baseline overhead differs between Python versions! Also, to fairly
compare older Python versions to Python 2.3, you may want to use python -O
for the older versions to avoid timing SET_LINENO instructions.
The baseline overhead differs between Python versions! Also, to
fairly compare older Python versions to Python 2.3, you may want to
use Python's \programopt{-O} option for the older versions to avoid
timing \code{SET_LINENO} instructions.
\subsection{Examples}
Here are two example sessions (one using the command line, one using the
module interface) that compare the cost of using \function{hasattr()}
vs. try/except to test for missing and present object attributes.
Here are two example sessions (one using the command line, one using
the module interface) that compare the cost of using
\function{hasattr()} vs. \keyword{try}/\keyword{except} to test for
missing and present object attributes.
\begin{verbatim}
\% timeit.py 'try:' ' str.__nonzero__' 'except AttributeError:' ' pass'
% timeit.py 'try:' ' str.__nonzero__' 'except AttributeError:' ' pass'
100000 loops, best of 3: 15.7 usec per loop
\% timeit.py 'if hasattr(str, "__nonzero__"): pass'
% timeit.py 'if hasattr(str, "__nonzero__"): pass'
100000 loops, best of 3: 4.26 usec per loop
\% timeit.py 'try:' ' int.__nonzero__' 'except AttributeError:' ' pass'
% timeit.py 'try:' ' int.__nonzero__' 'except AttributeError:' ' pass'
1000000 loops, best of 3: 1.43 usec per loop
\% timeit.py 'if hasattr(int, "__nonzero__"): pass'
% timeit.py 'if hasattr(int, "__nonzero__"): pass'
100000 loops, best of 3: 2.23 usec per loop
\end{verbatim}
@ -153,9 +164,9 @@ vs. try/except to test for missing and present object attributes.
>>> import timeit
>>> s = """\
... try:
... str.__nonzero__
... str.__nonzero__
... except AttributeError:
... pass
... pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
@ -168,9 +179,9 @@ vs. try/except to test for missing and present object attributes.
4.85 usec/pass
>>> s = """\
... try:
... int.__nonzero__
... int.__nonzero__
... except AttributeError:
... pass
... pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)