@c Copyright (C) 1996-2013 John W. Eaton
@c
@c This file is part of Octave.
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@c your option) any later version.
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@c for more details.
@c 
@c You should have received a copy of the GNU General Public License
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@node Debugging
@chapter Debugging

Octave includes a built-in debugger to aid in the development of
scripts.  This can be used to interrupt the execution of an Octave script
at a certain point, or when certain conditions are met.  Once execution
has stopped, and debug mode is entered, the symbol table at the point
where execution has stopped can be examined and modified to check for
errors.

The normal command-line editing and history functions are available in
debug mode.

@menu
* Entering Debug Mode::
* Leaving Debug Mode::
* Breakpoints::
* Debug Mode::
* Call Stack::
* Profiling::
* Profiler Example::
@end menu

@node Entering Debug Mode
@section Entering Debug Mode

There are two basic means of interrupting the execution of an Octave
script.  These are breakpoints (@pxref{Breakpoints}), discussed in the next
section, and interruption based on some condition.

Octave supports three means to stop execution based on the values set in
the functions @code{debug_on_interrupt}, @code{debug_on_warning}, and
@code{debug_on_error}.

@DOCSTRING(debug_on_interrupt)

@DOCSTRING(debug_on_warning)

@DOCSTRING(debug_on_error)

@node Leaving Debug Mode
@section Leaving Debug Mode

Use either @code{dbcont} or @code{return} to leave the debug mode and
continue the normal execution of the script.

@DOCSTRING(dbcont)

To quit debug mode and return directly to the prompt without executing
any additional code use @code{dbquit}.

@DOCSTRING(dbquit)

Finally, typing @code{exit} or @code{quit} at the debug prompt will
result in Octave terminating normally.

@node Breakpoints
@section Breakpoints

Breakpoints can be set in any m-file function by using the @code{dbstop}
function.

@DOCSTRING(dbstop)

@noindent
Breakpoints in class methods are also supported (e.g.,
@code{dbstop ("@@class/method")}).  However, breakpoints cannot be set in
built-in functions (e.g., @code{sin}, etc.) or dynamically loaded functions
(i.e., oct-files).

To set a breakpoint immediately upon entering a function use line number 1, or
omit the line number entirely and just give the function name.  When setting
the breakpoint Octave will ignore the leading comment block, and the breakpoint
will be set on the first executable statement in the function.  For example:

@example
@group
dbstop ("asind", 1)
@result{} 29
@end group
@end example

@noindent
Note that the return value of @code{29} means that the breakpoint was
effectively set to line 29.  The status of breakpoints in a function can
be queried with @code{dbstatus}.

@DOCSTRING(dbstatus)

@noindent
Reusing the previous example, @code{dbstatus ("asind")} will return
29.  The breakpoints listed can then be cleared with the @code{dbclear}
function.

@DOCSTRING(dbclear)

@noindent

A breakpoint may also be set in a subfunction.  For example, if a file contains
the functions

@example
@group
function y = func1 (x)
  y = func2 (x);
endfunction
function y = func2 (x)
  y = x + 1;
endfunction
@end group
@end example

@noindent
then a breakpoint can be set at the start of the subfunction directly with

@example
@group
dbstop (["func1", filemarker(), "func2"])
@result{} 5
@end group
@end example

Note that @code{filemarker} returns the character that marks subfunctions from
the file containing them.  Unless the default has been changed this character
is @samp{>}.  Thus, a quicker and more normal way to set the breakpoint would
be

@example
dbstop func1>func2
@end example

Another simple way of setting a breakpoint in an Octave script is the
use of the @code{keyboard} function.

@DOCSTRING(keyboard)

@noindent
The @code{keyboard} function is placed in a script at the point where the user
desires that the execution be stopped.  It automatically sets the running
script into the debug mode.

@node Debug Mode
@section Debug Mode

There are three additional support functions that allow the user to
find out where in the execution of a script Octave entered the debug
mode, and to print the code in the script surrounding the point where
Octave entered debug mode.

@DOCSTRING(dbwhere)

@DOCSTRING(dbtype)

@DOCSTRING(dblist)

You may also use @code{isdebugmode} to determine whether the debugger is
currently active.

@DOCSTRING(isdebugmode)

Debug mode also allows single line stepping through a function using
the command @code{dbstep}.

@DOCSTRING(dbstep)

When in debug mode the @key{RETURN} key will execute the last entered command.
This is useful, for example, after hitting a breakpoint and entering
@code{dbstep} once.  After that, one can advance line by line through the code
with only a single key stroke.

@node Call Stack
@section Call Stack

The function being debugged may be the leaf node of a series of function calls.
After examining values in the current subroutine it may turn out that the
problem occurred in earlier pieces of code.  Use @code{dbup} and @code{dbdown}
to move up and down through the series of function calls to locate where
variables first took on the wrong values.  @code{dbstack} shows the entire
series of function calls and at what level debugging is currently taking place.

@DOCSTRING(dbstack)

@DOCSTRING(dbup)

@DOCSTRING(dbdown)

@node Profiling
@section Profiling
@cindex profiler
@cindex code profiling

Octave supports profiling of code execution on a per-function level.  If
profiling is enabled, each call to a function (supporting built-ins,
operators, functions in oct- and mex-files, user-defined functions in
Octave code and anonymous functions) is recorded while running Octave
code.  After that, this data can aid in analyzing the code behavior, and
is in particular helpful for finding ``hot spots'' in the code which use
up a lot of computation time and are the best targets to spend
optimization efforts on.

The main command for profiling is @code{profile}, which can be used to
start or stop the profiler and also to query collected data afterwards.
The data is returned in an Octave data structure which can then be
examined or further processed by other routines or tools.

@DOCSTRING(profile)

An easy way to get an overview over the collected data is
@code{profshow}.  This function takes the profiler data returned by
@code{profile} as input and prints a flat profile, for instance:

@example
@group
 Function Attr     Time (s)        Calls
----------------------------------------
   >myfib    R        2.195        13529
binary <=             0.061        13529
 binary -             0.050        13528
 binary +             0.026         6764
@end group
@end example

This shows that most of the run time was spent executing the function
@samp{myfib}, and some minor proportion evaluating the listed binary
operators.  Furthermore, it is shown how often the function was called
and the profiler also records that it is recursive.

@DOCSTRING(profshow)

@DOCSTRING(profexplore)

@node Profiler Example
@section Profiler Example

Below, we will give a short example of a profiler session.
@xref{Profiling}, for the documentation of the profiler functions in
detail.  Consider the code:

@example
global N A;

N = 300;
A = rand (N, N);

function xt = timesteps (steps, x0, expM)
  global N;

  if (steps == 0)
    xt = NA (N, 0);
  else
    xt = NA (N, steps);
    x1 = expM * x0;
    xt(:, 1) = x1;
    xt(:, 2 : end) = timesteps (steps - 1, x1, expM);
  endif
endfunction

function foo ()
  global N A;

  initial = @@(x) sin (x);
  x0 = (initial (linspace (0, 2 * pi, N)))';

  expA = expm (A);
  xt = timesteps (100, x0, expA);
endfunction

function fib = bar (N)
  if (N <= 2)
    fib = 1;
  else
    fib = bar (N - 1) + bar (N - 2);
  endif
endfunction
@end example

If we execute the two main functions, we get:

@example
@group
tic; foo; toc;
@result{} Elapsed time is 2.37338 seconds.

tic; bar (20); toc;
@result{} Elapsed time is 2.04952 seconds.
@end group
@end example

But this does not give much information about where this time is spent;
for instance, whether the single call to @code{expm} is more expensive
or the recursive time-stepping itself.  To get a more detailed picture,
we can use the profiler.

@example
@group
profile on;
foo;
profile off;

data = profile ("info");
profshow (data, 10);
@end group
@end example

This prints a table like:

@example
@group
   #  Function Attr     Time (s)        Calls
---------------------------------------------
   7      expm             1.034            1
   3  binary *             0.823          117
  41  binary \             0.188            1
  38  binary ^             0.126            2
  43 timesteps    R        0.111          101
  44        NA             0.029          101
  39  binary +             0.024            8
  34      norm             0.011            1
  40  binary -             0.004          101
  33   balance             0.003            1
@end group
@end example

The entries are the individual functions which have been executed (only
the 10 most important ones), together with some information for each of
them.  The entries like @samp{binary *} denote operators, while other
entries are ordinary functions.  They include both built-ins like
@code{expm} and our own routines (for instance @code{timesteps}).  From
this profile, we can immediately deduce that @code{expm} uses up the
largest proportion of the processing time, even though it is only called
once.  The second expensive operation is the matrix-vector product in the
routine @code{timesteps}.  @footnote{We only know it is the binary
multiplication operator, but fortunately this operator appears only at
one place in the code and thus we know which occurrence takes so much
time.  If there were multiple places, we would have to use the
hierarchical profile to find out the exact place which uses up the time
which is not covered in this example.}

Timing, however, is not the only information available from the profile.
The attribute column shows us that @code{timesteps} calls itself
recursively.  This may not be that remarkable in this example (since it's
clear anyway), but could be helpful in a more complex setting.  As to the
question of why is there a @samp{binary \} in the output, we can easily
shed some light on that too.  Note that @code{data} is a structure array
(@ref{Structure Arrays}) which contains the field @code{FunctionTable}.
This stores the raw data for the profile shown.  The number in the first
column of the table gives the index under which the shown function can
be found there.  Looking up @code{data.FunctionTable(41)} gives:

@example
@group
  scalar structure containing the fields:

    FunctionName = binary \
    TotalTime =  0.18765
    NumCalls =  1
    IsRecursive = 0
    Parents =  7
    Children = [](1x0)
@end group
@end example

Here we see the information from the table again, but have additional
fields @code{Parents} and @code{Children}.  Those are both arrays, which
contain the indices of functions which have directly called the function
in question (which is entry 7, @code{expm}, in this case) or been called
by it (no functions).  Hence, the backslash operator has been used
internally by @code{expm}.

Now let's take a look at @code{bar}.  For this, we start a fresh
profiling session (@code{profile on} does this; the old data is removed
before the profiler is restarted):

@example
@group
profile on;
bar (20);
profile off;

profshow (profile ("info"));
@end group
@end example

This gives:

@example
@group
   #            Function Attr     Time (s)        Calls
-------------------------------------------------------
   1                 bar    R        2.091        13529
   2           binary <=             0.062        13529
   3            binary -             0.042        13528
   4            binary +             0.023         6764
   5             profile             0.000            1
   8               false             0.000            1
   6              nargin             0.000            1
   7           binary !=             0.000            1
   9 __profiler_enable__             0.000            1
@end group
@end example

Unsurprisingly, @code{bar} is also recursive.  It has been called 13,529
times in the course of recursively calculating the Fibonacci number in a
suboptimal way, and most of the time was spent in @code{bar} itself.

Finally, let's say we want to profile the execution of both @code{foo}
and @code{bar} together.  Since we already have the run-time data
collected for @code{bar}, we can restart the profiler without clearing
the existing data and collect the missing statistics about @code{foo}.
This is done by:

@example
@group
profile resume;
foo;
profile off;

profshow (profile ("info"), 10);
@end group
@end example

As you can see in the table below, now we have both profiles mixed
together.

@example
@group
   #  Function Attr     Time (s)        Calls
---------------------------------------------
   1       bar    R        2.091        13529
  16      expm             1.122            1
  12  binary *             0.798          117
  46  binary \             0.185            1
  45  binary ^             0.124            2
  48 timesteps    R        0.115          101
   2 binary <=             0.062        13529
   3  binary -             0.045        13629
   4  binary +             0.041         6772
  49        NA             0.036          101
@end group
@end example