--- a/Doc/lib/libprofile.tex	Fri Feb 13 06:48:05 1998 +0000
+++ b/Doc/lib/libprofile.tex	Fri Feb 13 06:58:54 1998 +0000
@@ -106,10 +106,10 @@
 To profile an application with a main entry point of \samp{foo()}, you
 would add the following to your module:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 import profile
 profile.run("foo()")
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The above action would cause \samp{foo()} to be run, and a series of
 informative lines (the profile) to be printed.  The above approach is
@@ -118,10 +118,10 @@
 can supply a file name as the second argument to the \code{run()}
 function:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 import profile
 profile.run("foo()", 'fooprof')
-\end{verbatim}\ecode
+\end{verbatim}
 %
 \code{profile.py} can also be invoked as
 a script to profile another script.  For example:
@@ -131,10 +131,10 @@
 \code{pstats} module.  Typically you would load the statistics data as
 follows:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 import pstats
 p = pstats.Stats('fooprof')
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The class \code{Stats} (the above code just created an instance of
 this class) has a variety of methods for manipulating and printing the
@@ -142,9 +142,9 @@
 \code{profile.run()} above, what was printed was the result of three
 method calls:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.strip_dirs().sort_stats(-1).print_stats()
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The first method removed the extraneous path from all the module
 names. The second method sorted all the entries according to the
@@ -152,18 +152,18 @@
 with the semantics of the old profiler).  The third method printed out
 all the statistics.  You might try the following sort calls:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.sort_stats('name')
 p.print_stats()
-\end{verbatim}\ecode
+\end{verbatim}
 %
 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:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.sort_stats('cumulative').print_stats(10)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 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
@@ -172,26 +172,26 @@
 If you were looking to see what functions were looping a lot, and
 taking a lot of time, you would do:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.sort_stats('time').print_stats(10)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 to sort according to time spent within each function, and then print
 the statistics for the top ten functions.
 
 You might also try:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.sort_stats('file').print_stats('__init__')
-\end{verbatim}\ecode
+\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 \code{__init__} in them).  As one final example, you could try:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.sort_stats('time', 'cum').print_stats(.5, 'init')
-\end{verbatim}\ecode
+\end{verbatim}
 %
 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.
@@ -202,19 +202,19 @@
 If you wondered what functions called the above functions, you could
 now (\samp{p} is still sorted according to the last criteria) do:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.print_callers(.5, 'init')
-\end{verbatim}\ecode
+\end{verbatim}
 %
 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:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 p.print_callees()
 p.add('fooprof')
-\end{verbatim}\ecode
+\end{verbatim}
 %
 \section{What Is Deterministic Profiling?}
 \nodename{Deterministic Profiling}
@@ -251,7 +251,7 @@
 
 \section{Reference Manual}
 
-\renewcommand{\indexsubitem}{(profiler function)}
+\setindexsubitem{(profiler function)}
 
 The primary entry point for the profiler is the global function
 \code{profile.run()}.  It is typically used to create any profile
@@ -273,7 +273,7 @@
 standard name string (file/line/function-name) that is presented in
 each line.  The following is a typical output from such a call:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
       main()
       2706 function calls (2004 primitive calls) in 4.504 CPU seconds
 
@@ -283,7 +283,7 @@
      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)
  ...
-\end{verbatim}\ecode
+\end{verbatim}
 
 The first line indicates that this profile was generated by the call:\\
 \code{profile.run('main()')}, and hence the exec'ed string is
@@ -348,7 +348,7 @@
 
 \subsection{The \sectcode{Stats} Class}
 
-\renewcommand{\indexsubitem}{(Stats method)}
+\setindexsubitem{(Stats method)}
 
 \begin{funcdesc}{strip_dirs}{}
 This method for the \code{Stats} class removes all leading path information
@@ -447,17 +447,17 @@
 defined by the \code{re} module).  If several restrictions are
 provided, then they are applied sequentially.  For example:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 print_stats(.1, "foo:")
-\end{verbatim}\ecode
+\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
 contrast, the command:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 print_stats("foo:", .1)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 would limit the list to all functions having file names \samp{.*foo:},
 and then proceed to only print the first 10\% of them.
@@ -487,10 +487,10 @@
 return the instance that is being processed, so that the commands can
 be strung together.  For example:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 pstats.Stats('foofile').strip_dirs().sort_stats('cum') \
                        .print_stats().ignore()
-\end{verbatim}\ecode
+\end{verbatim}
 %
 would perform all the indicated functions, but it would not return
 the final reference to the \code{Stats} instance.%
@@ -551,27 +551,27 @@
 be used to obtain this constant for a given platform (see discussion
 in section Limitations above).
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 import profile
 pr = profile.Profile()
 pr.calibrate(100)
 pr.calibrate(100)
 pr.calibrate(100)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The argument to calibrate() is the number of times to try to do the
 sample calls to get the CPU times.  If your computer is \emph{very}
 fast, you might have to do:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 pr.calibrate(1000)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 or even:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 pr.calibrate(10000)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The object of this exercise is to get a fairly consistent result.
 When you have a consistent answer, you are ready to use that number in
@@ -584,7 +584,7 @@
 class should be modified to install the calibration constant on a Sun
 Sparcstation 1000:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 def trace_dispatch(self, frame, event, arg):
     t = self.timer()
     t = t[0] + t[1] - self.t - .00053 # Calibration constant
@@ -596,14 +596,14 @@
         r = self.timer()
         self.t = r[0] + r[1] - t # put back unrecorded delta
     return
-\end{verbatim}\ecode
+\end{verbatim}
 %
 Note that if there is no calibration constant, then the line
 containing the callibration constant should simply say:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 t = t[0] + t[1] - self.t  # no calibration constant
-\end{verbatim}\ecode
+\end{verbatim}
 %
 You can also achieve the same results using a derived class (and the
 profiler will actually run equally fast!!), but the above method is
@@ -632,9 +632,9 @@
 the constructor for the class.  Consider passing the name of a
 function to call into the constructor:
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 pr = profile.Profile(your_time_func)
-\end{verbatim}\ecode
+\end{verbatim}
 %
 The resulting profiler will call \code{your_time_func()} instead of
 \code{os.times()}.  The function should return either a single number
@@ -664,7 +664,7 @@
 user's code.  It is also a lot more accurate than the old profiler, as
 it does not charge all its overhead time to the user's code.
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 class OldProfile(Profile):
 
     def trace_dispatch_exception(self, frame, t):
@@ -714,7 +714,7 @@
                       callers[func_caller]
                 nc = nc + callers[func_caller]
             self.stats[nor_func] = nc, nc, tt, ct, nor_callers
-\end{verbatim}\ecode
+\end{verbatim}
 %
 \subsection{HotProfile Class}
 
@@ -725,7 +725,7 @@
 the basic profiler is so fast, that is probably not worth the savings
 to give up the data, but this class still provides a nice example.
 
-\bcode\begin{verbatim}
+\begin{verbatim}
 class HotProfile(Profile):
 
     def trace_dispatch_exception(self, frame, t):
@@ -761,4 +761,4 @@
             nc, tt = self.timings[func]
             nor_func = self.func_normalize(func)
             self.stats[nor_func] = nc, nc, tt, 0, {}
-\end{verbatim}\ecode
+\end{verbatim}