AMK's megapatch:
* \bcode, \ecode added everywhere
* \label{module-foo} added everywhere
* A few \seealso sections added.
* Indentation fixed inside verbatim in lib*tex files
diff --git a/Doc/lib/libprofile.tex b/Doc/lib/libprofile.tex
index 7cd3c6b..2f69170 100644
--- a/Doc/lib/libprofile.tex
+++ b/Doc/lib/libprofile.tex
@@ -103,11 +103,11 @@
To profile an application with a main entry point of \samp{foo()}, you
would add the following to your module:
-\begin{verbatim}
- import profile
- profile.run("foo()")
-\end{verbatim}
-
+\bcode\begin{verbatim}
+import profile
+profile.run("foo()")
+\end{verbatim}\ecode
+%
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
most useful when working with the interpreter. If you would like to
@@ -115,11 +115,11 @@
can supply a file name as the second argument to the \code{run()}
function:
-\begin{verbatim}
- import profile
- profile.run("foo()", 'fooprof')
-\end{verbatim}
-
+\bcode\begin{verbatim}
+import profile
+profile.run("foo()", 'fooprof')
+\end{verbatim}\ecode
+%
\code{profile.py} can also be invoked as
a script to profile another script. For example:
\code{python /usr/local/lib/python1.4/profile.py myscript.py}
@@ -128,40 +128,40 @@
\code{pstats} module. Typically you would load the statistics data as
follows:
-\begin{verbatim}
- import pstats
- p = pstats.Stats('fooprof')
-\end{verbatim}
-
+\bcode\begin{verbatim}
+import pstats
+p = pstats.Stats('fooprof')
+\end{verbatim}\ecode
+%
The class \code{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
\code{profile.run()} above, what was printed was the result of three
method calls:
-\begin{verbatim}
- p.strip_dirs().sort_stats(-1).print_stats()
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.strip_dirs().sort_stats(-1).print_stats()
+\end{verbatim}\ecode
+%
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 (this is to comply
with the semantics of the old profiler). The third method printed out
all the statistics. You might try the following sort calls:
-\begin{verbatim}
- p.sort_stats('name')
- p.print_stats()
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.sort_stats('name')
+p.print_stats()
+\end{verbatim}\ecode
+%
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:
-\begin{verbatim}
- p.sort_stats('cumulative').print_stats(10)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.sort_stats('cumulative').print_stats(10)
+\end{verbatim}\ecode
+%
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.
@@ -169,27 +169,27 @@
If you were looking to see what functions were looping a lot, and
taking a lot of time, you would do:
-\begin{verbatim}
- p.sort_stats('time').print_stats(10)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.sort_stats('time').print_stats(10)
+\end{verbatim}\ecode
+%
to sort according to time spent within each function, and then print
the statistics for the top ten functions.
You might also try:
-\begin{verbatim}
- p.sort_stats('file').print_stats('__init__')
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.sort_stats('file').print_stats('__init__')
+\end{verbatim}\ecode
+%
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:
-\begin{verbatim}
- p.sort_stats('time', 'cum').print_stats(.5, 'init')
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.sort_stats('time', 'cum').print_stats(.5, 'init')
+\end{verbatim}\ecode
+%
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: \samp{.5})
@@ -199,21 +199,20 @@
If you wondered what functions called the above functions, you could
now (\samp{p} is still sorted according to the last criteria) do:
-\begin{verbatim}
- p.print_callers(.5, 'init')
-\end{verbatim}
-
+\bcode\begin{verbatim}
+p.print_callers(.5, 'init')
+\end{verbatim}\ecode
+%
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:
-\begin{verbatim}
- p.print_callees()
- p.add('fooprof')
-\end{verbatim}
-
-
+\bcode\begin{verbatim}
+p.print_callees()
+p.add('fooprof')
+\end{verbatim}\ecode
+%
\section{What Is Deterministic Profiling?}
\nodename{Deterministic Profiling}
@@ -272,7 +271,7 @@
each line. The following is a typical output from such a call:
\small{
-\begin{verbatim}
+\bcode\begin{verbatim}
main()
2706 function calls (2004 primitive calls) in 4.504 CPU seconds
@@ -282,7 +281,7 @@
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}
+\end{verbatim}\ecode
}
The first line indicates that this profile was generated by the call:\\
@@ -446,18 +445,18 @@
several restrictions are provided, then they are applied sequentially.
For example:
-\begin{verbatim}
- print_stats(.1, "foo:")
-\end{verbatim}
-
+\bcode\begin{verbatim}
+print_stats(.1, "foo:")
+\end{verbatim}\ecode
+%
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:
-\begin{verbatim}
- print_stats("foo:", .1)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+print_stats("foo:", .1)
+\end{verbatim}\ecode
+%
would limit the list to all functions having file names \samp{.*foo:},
and then proceed to only print the first 10\% of them.
\end{funcdesc}
@@ -486,11 +485,11 @@
return the instance that is being processed, so that the commands can
be strung together. For example:
-\begin{verbatim}
+\bcode\begin{verbatim}
pstats.Stats('foofile').strip_dirs().sort_stats('cum') \
.print_stats().ignore()
-\end{verbatim}
-
+\end{verbatim}\ecode
+%
would perform all the indicated functions, but it would not return
the final reference to the \code{Stats} instance.%
\footnote{
@@ -550,28 +549,28 @@
be used to obtain this constant for a given platform (see discussion
in section Limitations above).
-\begin{verbatim}
- import profile
- pr = profile.Profile()
- pr.calibrate(100)
- pr.calibrate(100)
- pr.calibrate(100)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+import profile
+pr = profile.Profile()
+pr.calibrate(100)
+pr.calibrate(100)
+pr.calibrate(100)
+\end{verbatim}\ecode
+%
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:
-\begin{verbatim}
- pr.calibrate(1000)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+pr.calibrate(1000)
+\end{verbatim}\ecode
+%
or even:
-\begin{verbatim}
- pr.calibrate(10000)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+pr.calibrate(10000)
+\end{verbatim}\ecode
+%
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
the source code. For a Sun Sparcstation 1000 running Solaris 2.3, the
@@ -583,27 +582,27 @@
class should be modified to install the calibration constant on a Sun
Sparcstation 1000:
-\begin{verbatim}
- def trace_dispatch(self, frame, event, arg):
+\bcode\begin{verbatim}
+def trace_dispatch(self, frame, event, arg):
+ t = self.timer()
+ t = t[0] + t[1] - self.t - .00053 # Calibration constant
+
+ if self.dispatch[event](frame,t):
t = self.timer()
- t = t[0] + t[1] - self.t - .00053 # Calibration constant
-
- if self.dispatch[event](frame,t):
- t = self.timer()
- self.t = t[0] + t[1]
- else:
- r = self.timer()
- self.t = r[0] + r[1] - t # put back unrecorded delta
- return
-\end{verbatim}
-
+ self.t = t[0] + t[1]
+ else:
+ r = self.timer()
+ self.t = r[0] + r[1] - t # put back unrecorded delta
+ return
+\end{verbatim}\ecode
+%
Note that if there is no calibration constant, then the line
containing the callibration constant should simply say:
-\begin{verbatim}
- t = t[0] + t[1] - self.t # no calibration constant
-\end{verbatim}
-
+\bcode\begin{verbatim}
+t = t[0] + t[1] - self.t # no calibration constant
+\end{verbatim}\ecode
+%
You can also achieve the same results using a derived class (and the
profiler will actually run equally fast!!), but the above method is
the simplest to use. I could have made the profiler ``self
@@ -631,10 +630,10 @@
the constructor for the class. Consider passing the name of a
function to call into the constructor:
-\begin{verbatim}
- pr = profile.Profile(your_time_func)
-\end{verbatim}
-
+\bcode\begin{verbatim}
+pr = profile.Profile(your_time_func)
+\end{verbatim}\ecode
+%
The resulting profiler will call \code{your_time_func()} instead of
\code{os.times()}. The function should return either a single number
or a list of numbers (like what \code{os.times()} returns). If the
@@ -663,7 +662,7 @@
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.
-\begin{verbatim}
+\bcode\begin{verbatim}
class OldProfile(Profile):
def trace_dispatch_exception(self, frame, t):
@@ -713,9 +712,8 @@
callers[func_caller]
nc = nc + callers[func_caller]
self.stats[nor_func] = nc, nc, tt, ct, nor_callers
-\end{verbatim}
-
-
+\end{verbatim}\ecode
+%
\subsection{HotProfile Class}
This profiler is the fastest derived profile example. It does not
@@ -725,7 +723,7 @@
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.
-\begin{verbatim}
+\bcode\begin{verbatim}
class HotProfile(Profile):
def trace_dispatch_exception(self, frame, t):
@@ -761,4 +759,4 @@
nc, tt = self.timings[func]
nor_func = self.func_normalize(func)
self.stats[nor_func] = nc, nc, tt, 0, {}
-\end{verbatim}
+\end{verbatim}\ecode