more markup nits

This commit is contained in:
Fred Drake 2004-03-23 21:40:07 +00:00
parent 69200fa85b
commit fee6f33e08
1 changed files with 13 additions and 12 deletions

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@ -104,15 +104,15 @@ 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 \samp{foo()}, you
would add the following to your module:
To profile an application with a main entry point of \function{foo()},
you would add the following to your module:
\begin{verbatim}
import profile
profile.run('foo()')
\end{verbatim}
The above action would cause \samp{foo()} to be run, and a series of
The above action would cause \function{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
@ -137,8 +137,9 @@ python /usr/local/lib/python1.5/profile.py myscript.py
profile.py [-o output_file] [-s sort_order]
\end{verbatim}
\samp{-s} only applies to stdout (i.e. \samp{-o} is not supplied.
Look in the \class{Stats} documentation for valid sort values.
\programopt{-s} only applies to standard output (\programopt{-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
\module{pstats} module. Typically you would load the statistics data as
@ -151,7 +152,7 @@ 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 \samp{p}. When you ran
data that was just read into \code{p}. When you ran
\function{profile.run()} above, what was printed was the result of three
method calls:
@ -199,8 +200,8 @@ p.sort_stats('file').print_stats('__init__')
\end{verbatim}
This will sort all the statistics by file name, and then print out
statistics for only the class init methods ('cause they are spelled
with \samp{__init__} in them). As one final example, you could try:
statistics for only the class init methods (since they are spelled
with \code{__init__} in them). As one final example, you could try:
\begin{verbatim}
p.sort_stats('time', 'cum').print_stats(.5, 'init')
@ -213,7 +214,7 @@ of its original size, then only lines containing \code{init} are
maintained, and that sub-sub-list is printed.
If you wondered what functions called the above functions, you could
now (\samp{p} is still sorted according to the last criteria) do:
now (\code{p} is still sorted according to the last criteria) do:
\begin{verbatim}
p.print_callers(.5, 'init')
@ -423,7 +424,7 @@ identifying the basis of a sort (example: \code{'time'} or
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, \samp{sort_stats('name', 'file')} will sort
before them. For example, \code{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.
@ -495,14 +496,14 @@ print_stats(.1, 'foo:')
\end{verbatim}
would first limit the printing to first 10\% of list, and then only
print functions that were part of filename \samp{.*foo:}. In
print functions that were part of filename \file{.*foo:}. In
contrast, the command:
\begin{verbatim}
print_stats('foo:', .1)
\end{verbatim}
would limit the list to all functions having file names \samp{.*foo:},
would limit the list to all functions having file names \file{.*foo:},
and then proceed to only print the first 10\% of them.
\end{methoddesc}