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<H1><a name="Python"></a>26 SWIG and Python</H1>
<!-- INDEX -->
<div class="sectiontoc">
<ul>
<li><a href="#Python_nn2">Overview</a>
<li><a href="#Python_nn3">Preliminaries</a>
<ul>
<li><a href="#Python_nn4">Running SWIG</a>
<li><a href="#Python_nn5">Getting the right header files</a>
<li><a href="#Python_nn6">Compiling a dynamic module</a>
<li><a href="#Python_nn7">Using distutils</a>
<li><a href="#Python_nn8">Static linking</a>
<li><a href="#Python_nn9">Using your module</a>
<li><a href="#Python_nn10">Compilation of C++ extensions</a>
<li><a href="#Python_nn11">Compiling for 64-bit platforms</a>
<li><a href="#Python_nn12">Building Python Extensions under Windows</a>
</ul>
<li><a href="#Python_nn13">A tour of basic C/C++ wrapping</a>
<ul>
<li><a href="#Python_nn14">Modules</a>
<li><a href="#Python_nn15">Functions</a>
<li><a href="#Python_nn16">Global variables</a>
<li><a href="#Python_nn17">Constants and enums</a>
<li><a href="#Python_nn18">Pointers</a>
<li><a href="#Python_nn19">Structures</a>
<li><a href="#Python_nn20">C++ classes</a>
<li><a href="#Python_nn21">C++ inheritance</a>
<li><a href="#Python_nn22">Pointers, references, values, and arrays</a>
<li><a href="#Python_nn23">C++ overloaded functions</a>
<li><a href="#Python_nn24">C++ operators</a>
<li><a href="#Python_nn25">C++ namespaces</a>
<li><a href="#Python_nn26">C++ templates</a>
<li><a href="#Python_nn27">C++ Smart Pointers</a>
<li><a href="#Python_nn27a">C++ Reference Counted Objects (ref/unref)</a>
</ul>
<li><a href="#Python_nn28">Further details on the Python class interface</a>
<ul>
<li><a href="#Python_nn29">Proxy classes</a>
<li><a href="#Python_nn30">Memory management</a>
<li><a href="#Python_nn31">Python 2.2 and classic classes</a>
</ul>
<li><a href="#directors">Cross language polymorphism</a>
<ul>
<li><a href="#Python_nn33">Enabling directors</a>
<li><a href="#Python_nn34">Director classes</a>
<li><a href="#Python_nn35">Ownership and object destruction</a>
<li><a href="#Python_nn36">Exception unrolling</a>
<li><a href="#Python_nn37">Overhead and code bloat</a>
<li><a href="#Python_nn38">Typemaps</a>
<li><a href="#Python_nn39">Miscellaneous</a>
</ul>
<li><a href="#Python_nn40">Common customization features</a>
<ul>
<li><a href="#Python_nn41">C/C++ helper functions</a>
<li><a href="#Python_nn42">Adding additional Python code</a>
<li><a href="#Python_nn43">Class extension with %extend</a>
<li><a href="#Python_nn44">Exception handling with %exception</a>
</ul>
<li><a href="#Python_nn45">Tips and techniques</a>
<ul>
<li><a href="#Python_nn46">Input and output parameters</a>
<li><a href="#Python_nn47">Simple pointers</a>
<li><a href="#Python_nn48">Unbounded C Arrays</a>
<li><a href="#Python_nn49">String handling</a>
<li><a href="#Python_nn50">Arrays</a>
<li><a href="#Python_nn51">String arrays</a>
<li><a href="#Python_nn52">STL wrappers</a>
</ul>
<li><a href="#Python_nn53">Typemaps</a>
<ul>
<li><a href="#Python_nn54">What is a typemap?</a>
<li><a href="#Python_nn55">Python typemaps</a>
<li><a href="#Python_nn56">Typemap variables</a>
<li><a href="#Python_nn57">Useful Python Functions</a>
</ul>
<li><a href="#Python_nn58">Typemap Examples</a>
<ul>
<li><a href="#Python_nn59">Converting  Python list to a char ** </a>
<li><a href="#Python_nn60">Expanding a Python object into multiple arguments</a>
<li><a href="#Python_nn61">Using typemaps to return arguments</a>
<li><a href="#Python_nn62">Mapping Python tuples into small arrays</a>
<li><a href="#Python_nn63">Mapping sequences to C arrays</a>
<li><a href="#Python_nn64">Pointer handling</a>
</ul>
<li><a href="#Python_nn65">Docstring Features</a>
<ul>
<li><a href="#Python_nn66">Module docstring</a>
<li><a href="#Python_nn67">%feature("autodoc")</a>
<ul>
<li><a href="#Python_nn68">%feature("autodoc", "0")</a>
<li><a href="#Python_nn69">%feature("autodoc", "1")</a>
<li><a href="#Python_nn70">%feature("autodoc", "docstring")</a>
</ul>
<li><a href="#Python_nn71">%feature("docstring")</a>
</ul>
<li><a href="#Python_nn72">Python Packages</a>
</ul>
</div>
<!-- INDEX -->


<p>
<b>Caution: This chapter is under repair!</b>
</p>

<p>
This chapter describes SWIG's support of Python.  SWIG is compatible
with most recent Python versions including Python 2.2 as well as older
versions dating back to Python 1.5.2.  For the best results, consider using Python
2.0 or newer.
</p>

<p>
This chapter covers most SWIG features, but certain low-level details
are covered in less depth than in earlier chapters.  At the
very least, make sure you read the "<a href="SWIG.html#SWIG">SWIG
Basics</a>" chapter.
</p>

<H2><a name="Python_nn2"></a>26.1 Overview</H2>


<p>
To build Python extension modules, SWIG uses a layered approach in which
parts of the extension module are defined in C and other parts are 
defined in Python.  The C layer contains low-level wrappers whereas Python code
is used to define high-level features.
</p>

<p>
This layered approach recognizes the fact that certain aspects of
extension building are better accomplished in each language (instead
of trying to do everything in C or C++). Furthermore, by generating code in both
languages, you get a lot more flexibility since you can enhance the extension
module with support code in either language.
</p>

<p>
In describing the Python interface, this chapter starts by covering the
basics of configuration, compiling, and installing Python modules.
Next, the Python interface to common C and C++ programming features is
described.  Advanced customization features such as typemaps are then
described followed by a discussion of low-level implementation
details.
</p>

<H2><a name="Python_nn3"></a>26.2 Preliminaries</H2>


<H3><a name="Python_nn4"></a>26.2.1 Running SWIG</H3>


<p>
Suppose that you defined a SWIG module such as the following:
</p>

<div class="code">
<pre>
%module example
%{
#include "header.h"
%}
int fact(int n);
</pre>
</div>

<p>
To build a Python module, run SWIG using the <tt>-python</tt> option :
</p>

<div class="shell"><pre>
$ swig -python example.i
</pre></div>

<p>
If building a C++ extension, add the <tt>-c++</tt> option:
</p>

<div class="shell"><pre>
$ swig -c++ -python example.i
</pre></div>

<p>
This creates two different files; a C/C++ source file <tt>example_wrap.c</tt> or
<tt>example_wrap.cxx</tt> and a Python source file <tt>example.py</tt>.   The generated
C source file contains the low-level wrappers that need to be compiled and linked with the
rest of your C/C++ application to create an extension module. The Python source file contains 
high-level support code. This is the file that you will import to use the module.
</p>

<p>
The name of the wrapper file is derived from the name of the input file.  For example, if the
input file is <tt>example.i</tt>, the name of the wrapper file is <tt>example_wrap.c</tt>.
To change this, you can use the <tt>-o</tt> option.   The name of the Python file is derived
from the module name specified with <tt>%module</tt>.  If the module name is <tt>example</tt>, then
a file <tt>example.py</tt> is created.
</p>

<H3><a name="Python_nn5"></a>26.2.2 Getting the right header files</H3>


<p>
In order to compile the C/C++ wrappers, the compiler needs the <tt>Python.h</tt> header file.
This file is usually contained in a directory such as 
</p>

<div class="diagram"><pre>
/usr/local/include/python2.0
</pre></div>

<p>
The exact location may vary on your machine, but the above location is
typical.  If you are not entirely sure where Python is installed, you
can run Python to find out.  For example:
</p>

<div class="targetlang">
<pre>
$ python
Python 2.1.1 (#1, Jul 23 2001, 14:36:06)
[GCC egcs-2.91.66 19990314/Linux (egcs-1.1.2 release)] on linux2
Type "copyright", "credits" or "license" for more information.
&gt;&gt;&gt; import sys
&gt;&gt;&gt; print sys.prefix
/usr/local
&gt;&gt;&gt;           
</pre>
</div>

<H3><a name="Python_nn6"></a>26.2.3 Compiling a dynamic module</H3>


<p>
The preferred approach to building an extension module is to compile it into
a shared object file or DLL.   To do this, you need to compile your program
using commands like this (shown for Linux):
</p>

<div class="shell"><pre>
$ swig -python example.i
$ gcc -c -fPIC example.c
$ gcc -c -fPIC example_wrap.c -I/usr/local/include/python2.0
$ gcc -shared example.o example_wrap.o -o _example.so
</pre></div>

<p>
The exact commands for doing this vary from platform to platform. 
However, SWIG tries to guess the right options when it is installed.  Therefore, 
you may want to start with one of the examples in the <tt>SWIG/Examples/python</tt> 
directory.   If that doesn't work, you will need to read the man-pages for
your compiler and linker to get the right set of options.  You might also
check the <a href="http://swig.cs.uchicago.edu/cgi-bin/wiki.pl">SWIG Wiki</a> for
additional information.
</p>

<p>
When linking the module, <b>the name of the output file has to match the name
of the module prefixed by an underscore</b>.  If the name of your module is "<tt>example</tt>", then the
name of the corresponding object file should be
"<tt>_example.so</tt>" or "<tt>_examplemodule.so</tt>".
The name of the module is specified using the <tt>%module</tt> directive or the 
<tt> -module</tt> command line option.
</p>

<p>
<b>Compatibility Note:</b> In SWIG-1.3.13 and earlier releases, module
names did not include the leading underscore.  This is because modules
were normally created as C-only extensions without the extra Python
support file (instead, creating Python code was supported as an optional
feature).  This has been changed in SWIG-1.3.14 and is consistent with
other Python extension modules.  For example, the <tt>socket</tt>
module actually consists of two files; <tt>socket.py</tt> and
<tt>_socket.so</tt>.  Many other built-in Python modules follow a similar convention.
</p>

<H3><a name="Python_nn7"></a>26.2.4 Using distutils</H3>


<H3><a name="Python_nn8"></a>26.2.5 Static linking</H3>


<p>
An alternative approach to dynamic linking is to rebuild the Python
interpreter with your extension module added to it.  In the past,
this approach was sometimes necessary due to limitations in dynamic loading
support on certain machines.  However, the situation has improved greatly
over the last few years and you should not consider this approach 
unless there is really no other option.
</p>

<p>
The usual procedure for adding a new module to Python involves finding
the Python source, adding an entry to the <tt>Modules/Setup</tt> file,
and rebuilding the interpreter using the Python Makefile.  However,
newer Python versions have changed the build process.  You may need to edit
the 'setup.py' file in the Python distribution instead.
</p>

<p>
In earlier versions of SWIG, the <tt>embed.i</tt> library file could be used to
rebuild the interpreter.  For example:
</p>

<div class="code"><pre>
%module example

%inline %{
extern int fact(int);
extern int mod(int, int);
extern double My_variable;
%}

%include embed.i       // Include code for a static version of Python

</pre></div>

<p>
The <tt>embed.i</tt> library file includes supporting code that
contains everything needed to rebuild Python. To rebuild the interpreter,
you simply do something like this:
</p>

<div class="shell"><pre>
$ swig -python example.i
$ gcc example.c example_wrap.c \
        -Xlinker -export-dynamic \
        -DHAVE_CONFIG_H -I/usr/local/include/python2.1 \
	-I/usr/local/lib/python2.1/config \
	-L/usr/local/lib/python2.1/config -lpython2.1 -lm -ldl \
	-o mypython

</pre></div>
<p>
You will need to supply the same libraries that were used to build Python the first
time.  This may include system libraries such as <tt>-lsocket</tt>, <tt>-lnsl</tt>,
and <tt>-lpthread</tt>.  Assuming this actually works, the new version of Python
should be identical to the default version except that your extension module will be
a built-in part of the interpreter.
</p>

<p>
<b>Comment:</b> In practice, you should probably try to avoid static
linking if possible. Some programmers may be inclined
to use static linking in the interest of getting better performance.
However, the performance gained by static linking tends to be rather
minimal in most situations (and quite frankly not worth the extra
hassle in the opinion of this author). 
</p>

<p>
<b>Compatibility note:</b> The <tt>embed.i</tt> library file is
deprecated and has not been maintained for several years.  Even though it
appears to "work" with Python 2.1, no future support is guaranteed.
If using static linking, you might want to rely on a different approach
(perhaps using distutils).
</p>

<H3><a name="Python_nn9"></a>26.2.6 Using your module</H3>


<p>
To use your module, simply use the Python <tt>import</tt> statement. If
all goes well, you will be able to this:
</p>

<div class="targetlang"><pre>
$ python
&gt;&gt;&gt; import example
&gt;&gt;&gt; example.fact(4)
24
&gt;&gt;&gt;
</pre></div>

<p>
A common error received by first-time users is the following:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
  File "example.py", line 2, in ?
    import _example
ImportError: No module named _example
</pre>
</div>

<p>
If you get this message, it means that you either forgot to compile the wrapper
code into an extension module or you didn't give the extension module the right
name.  Make sure that you compiled the wrappers into a module called <tt>_example.so</tt>.  And
don't forget the leading underscore (_).
</p>

<p>
Another possible error is the following:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
ImportError: dynamic module does not define init function (init_example)
&gt;&gt;&gt;                                                               
</pre>
</div>

<p>
This error is almost always caused when a bad name is given to the shared object file. 
For example, if you created a file <tt>example.so</tt> instead of <tt>_example.so</tt> you would 
get this error.  Alternatively, this error could arise if the name of the module is
inconsistent with the module name supplied with the <tt>%module</tt> directive.
Double-check the interface to make sure the module name and the shared object
filename match.  Another possible cause of this error is forgetting to link the SWIG-generated
wrapper code with the rest of your application when creating the extension module.
</p>

<p>
Another common error is something similar to the following:
</p>

<div class="targetlang">
<pre>
Traceback (most recent call last):
  File "example.py", line 3, in ?
    import example
ImportError: ./_example.so: undefined symbol: fact
</pre>
</div>

<p>
This error usually indicates that you forgot to include some object
files or libraries in the linking of the shared library file.  Make
sure you compile both the SWIG wrapper file and your original program
into a shared library file.  Make sure you pass all of the required libraries
to the linker.  
</p>

<p>
Sometimes unresolved symbols occur because a wrapper has been created
for a function that doesn't actually exist in a library.  This usually
occurs when a header file includes a declaration for a function that
was never actually implemented or it was removed from a library
without updating the header file.  To fix this, you can either edit
the SWIG input file to remove the offending declaration or you can use
the <tt>%ignore</tt> directive to ignore the declaration.
</p>

<p>
Finally, suppose that your extension module is linked with another library like this:
</p>

<div class="shell">
<pre>
$ gcc -shared example.o example_wrap.o -L/home/beazley/projects/lib <b>-lfoo</b> \
      -o _example.so
</pre>
</div>

<p>
If the <tt>foo</tt> library is compiled as a shared library, you might encounter the following
problem when you try to use your module:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
ImportError: libfoo.so: cannot open shared object file: No such file or directory
&gt;&gt;&gt;                 
</pre>
</div>

<p>
This error is generated because the dynamic linker can't locate the
<tt>libfoo.so</tt> library.  When shared libraries are loaded, the
system normally only checks a few standard locations such as
<tt>/usr/lib</tt> and <tt>/usr/local/lib</tt>.   To fix this problem,
there are several things you can do.  First, you can recompile your extension
module with extra path information. For example, on Linux you can do this:
</p>

<div class="shell">
<pre>
$ gcc -shared example.o example_wrap.o -L/home/beazley/projects/lib -lfoo \
      <b>-Xlinker -rpath /home/beazley/projects/lib </b> \
      -o _example.so
</pre>
</div>

<p>
Alternatively, you can set the <tt>LD_LIBRARY_PATH</tt> environment variable to
include the directory with your shared libraries. 
If setting <tt>LD_LIBRARY_PATH</tt>, be aware that setting this variable can introduce
a noticeable performance impact on all other applications that you run.
To set it only for Python, you might want to do this instead:
</p>

<div class="shell">
<pre>
$ env LD_LIBRARY_PATH=/home/beazley/projects/lib python
</pre>
</div>

<p>
Finally, you can use a command such as <tt>ldconfig</tt> (Linux) or
<tt>crle</tt> (Solaris) to add additional search paths to the default
system configuration (this requires root access and you will need to
read the man pages).
</p>

<H3><a name="Python_nn10"></a>26.2.7 Compilation of C++ extensions</H3>


<p>
Compilation of C++ extensions has traditionally been a tricky problem.
Since the Python interpreter is written in C, you need to take steps to
make sure C++ is properly initialized and that modules are compiled
correctly.
</p>

<p>
On most machines, C++ extension modules should be linked using the C++
compiler.  For example:
</p>

<div class="shell"><pre>
$ swig -c++ -python example.i
$ g++ -c example.cxx
$ g++ -c example_wrap.cxx -I/usr/local/include/python2.0
$ g++ -shared example.o example_wrap.o -o _example.so
</pre></div>

<p>
On some platforms, you could also need to generate
position-independent code (PIC), by using a compiler option such as -fPIC.
Notably, the x86_64 (Opteron and EM64T) platform requires it, and when
using the GNU Compiler Suite, you will need to modify the previous example
as follows:
</p>

<div class="shell"><pre>
$ swig -c++ -python example.i
$ g++ -fPIC -c example.cxx
$ g++ -fPIC -c example_wrap.cxx -I/usr/local/include/python2.0
$ g++ -shared example.o example_wrap.o -o _example.so
</pre></div>

<p>
In addition to this, you may need to include additional library
files to make it work.  For example, if you are using the Sun C++ compiler on
Solaris, you often need to add an extra library <tt>-lCrun</tt> like this:
</p>

<div class="shell"><pre>
$ swig -c++ -python example.i
$ CC -c example.cxx
$ CC -c example_wrap.cxx -I/usr/local/include/python2.0
$ CC -G example.o example_wrap.o -L/opt/SUNWspro/lib -o _example.so -lCrun
</pre></div>

<p>
Of course, the extra libraries to use are completely non-portable---you will 
probably need to do some experimentation.
</p>

<p>
Sometimes people have suggested that it is necessary to relink the
Python interpreter using the C++ compiler to make C++ extension modules work.
In the experience of this author, this has never actually appeared to be
necessary.   Relinking the interpreter with C++ really only includes the 
special run-time libraries described above---as long as you link your extension 
modules with these libraries, it should not be necessary to rebuild Python.
</p>

<p>
If you aren't entirely sure about the linking of a C++ extension, you
might look at an existing C++ program.  On many Unix machines, the
<tt>ldd</tt> command will list library dependencies.  This should give
you some clues about what you might have to include when you link your
extension module. For example:
</p>

<div class="shell">
<pre>
$ ldd swig
        libstdc++-libc6.1-1.so.2 =&gt; /usr/lib/libstdc++-libc6.1-1.so.2 (0x40019000)
        libm.so.6 =&gt; /lib/libm.so.6 (0x4005b000)
        libc.so.6 =&gt; /lib/libc.so.6 (0x40077000)
        /lib/ld-linux.so.2 =&gt; /lib/ld-linux.so.2 (0x40000000)
</pre>
</div>

<p>
As a final complication, a major weakness of C++ is that it does not
define any sort of standard for binary linking of libraries.  This
means that C++ code compiled by different compilers will not link
together properly as libraries nor is the memory layout of classes and
data structures implemented in any kind of portable manner.  In a
monolithic C++ program, this problem may be unnoticed.  However, in Python, it
is possible for different extension modules to be compiled with
different C++ compilers.  As long as these modules are self-contained,
this probably won't matter.  However, if these modules start sharing data,
you will need to take steps to avoid segmentation faults and other
erratic program behavior.   If working with lots of software components, you
might want to investigate using a more formal standard such as COM.
</p>

<H3><a name="Python_nn11"></a>26.2.8 Compiling for 64-bit platforms</H3>


<p>
On platforms that support 64-bit applications (Solaris, Irix, etc.),
special care is required when building extension modules.  On these
machines, 64-bit applications are compiled and linked using a different
set of compiler/linker options.  In addition, it is not generally possible to mix 
32-bit and 64-bit code together in the same application.
</p>

<p>
To utilize 64-bits, the Python executable will need to be recompiled
as a 64-bit application.  In addition, all libraries, wrapper code,
and every other part of your application will need to be compiled for
64-bits.  If you plan to use other third-party extension modules, they
will also have to be recompiled as 64-bit extensions.
</p>

<p>
If you are wrapping commercial software for which you have no source
code, you will be forced to use the same linking standard as used by
that software.  This may prevent the use of 64-bit extensions.  It may
also introduce problems on platforms that support more than one
linking standard (e.g., -o32 and -n32 on Irix).
</p>

<p> On the Linux x86_64 platform (Opteron or EM64T), besides of the
required compiler option -fPIC discussed above, you will need to be
careful about the libraries you link with or the library path you
use. In general, a Linux distribution will have two set of libraries,
one for native x86_64 programs (under /usr/lib64), and another for 32
bits compatibility (under /usr/lib). Also, the compiler options -m32
and -m64 allow you to choose the desired binary format for your python
extension.
</p>

<H3><a name="Python_nn12"></a>26.2.9 Building Python Extensions under Windows</H3>


<p>
Building a SWIG extension to Python under Windows is roughly
similar to the process used with Unix.   You will need to create a
DLL that can be loaded into the interpreter.  This section briefly
describes the use of SWIG with Microsoft Visual C++.   As a starting
point, many of SWIG's examples include project files.  You might want to 
take a quick look at these in addition to reading this section.
</p>

<p>
In Developer Studio, SWIG should be invoked as a custom build option.
This is usually done as follows:
</p>

<ul>
<li>Open up a new workspace and use the AppWizard to select a DLL
project.

<li>Add both the SWIG interface file (the .i file), any supporting C
files, and the name of the wrapper file that will be created by SWIG
(ie. <tt>example_wrap.c</tt>).  Note : If using C++, choose a
different suffix for the wrapper file such as
<tt>example_wrap.cxx</tt>. Don't worry if the wrapper file doesn't
exist yet--Developer Studio keeps a reference to it.

<li>Select the SWIG interface file and go to the settings menu.  Under
settings, select the "Custom Build" option.

<li>Enter "SWIG" in the description field.

<li>Enter "<tt>swig -python -o $(ProjDir)\$(InputName)_wrap.c $(InputPath)</tt>" in the "Build command(s) field"

<li>Enter "<tt>$(ProjDir)\$(InputName)_wrap.c</tt>" in the "Output files(s) field".

<li>Next, select the settings for the entire project and go to
"C++:Preprocessor". Add the include directories for your Python
installation under "Additional include directories".

<li>Define the symbol  __WIN32__ under preprocessor options.  

<li>Finally, select the settings for the entire project and go to
"Link Options".  Add the Python library file to your link libraries.
For example "python21.lib".  Also, set the name of the output file to
match the name of your Python module (ie. _example.dll).

<li>Build your project.
</ul>

<p>
If all went well, SWIG will be automatically invoked whenever
you build your project.  Any changes made to the interface file will
result in SWIG being automatically executed to produce a new version of
the wrapper file. 
</p>

<p>
To run your new Python extension, simply run Python
and use the <tt>import</tt> command as normal. For example :
</p>

<div class="targetlang"><pre>
$ python
&gt;&gt;&gt; import example
&gt;&gt;&gt; print example.fact(4)
24
&gt;&gt;&gt;
</pre></div>

<p>
If you get an <tt>ImportError</tt> exception when importing the module, you may
have forgotten to include aditional library files when you built your module.
If you get an access violation or some kind of general protection fault 
immediately upon import, you have a more serious problem.   This 
is often caused by linking your extension module against the wrong 
set of Win32 debug or thread libraries.  You will have to fiddle around with
the build options of project to try and track this down.
</p>

<p>
Some users have reported success in building extension modules using Cygwin
and other compilers.  However, the problem of building usable DLLs with these
compilers tends to be rather problematic.  For the latest information,
you may want to consult the <a href="http://swig.cs.uchicago.edu/cgi-bin/wiki.pl">
SWIG Wiki</a>.
</p>

<H2><a name="Python_nn13"></a>26.3 A tour of basic C/C++ wrapping</H2>


<p>
By default, SWIG tries to build a very natural Python interface
to your C/C++ code.  Functions are wrapped as functions, classes are wrapped as classes, and so forth.
This section briefly covers the essential aspects of this wrapping.
</p>

<H3><a name="Python_nn14"></a>26.3.1 Modules</H3>


<p>
The SWIG <tt>%module</tt> directive specifies the name of the Python
module. If you specify `<tt>%module example</tt>', then everything is
wrapped into a Python '<tt>example</tt>' module.  Underneath the covers,
this module consists of a Python source file <tt>example.py</tt> and a low-level
extension module <tt>_example.so</tt>. When choosing a
module name, make sure you don't use the same name as a built-in
Python command or standard module name.  
</p>

<H3><a name="Python_nn15"></a>26.3.2 Functions</H3>


<p>
Global functions are wrapped as new Python built-in functions.  For example,
</p>

<div class="code"><pre>
%module example
int fact(int n);
</pre></div>

<p>
creates a built-in function <tt>example.fact(n)</tt> that works exactly
like you think it does:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; print example.fact(4)
24
&gt;&gt;&gt;
</pre></div>

<H3><a name="Python_nn16"></a>26.3.3 Global variables</H3>


<p>
C/C++ global variables are fully supported by SWIG.  However, the underlying
mechanism is somewhat different than you might expect due to the way that
Python assignment works.  When you type the following in Python
</p>

<div class="targetlang"><pre>
a = 3.4
</pre></div>

<p>
"a" becomes a name for an object containing the value 3.4. If you later type
</p>

<div class="targetlang"><pre>
b = a
</pre></div>

<p>
then "a" and "b" are both names for the object containing the value
3.4. Thus, there is only one object containing 3.4 and "a"
and "b" are both names that refer to it. This is quite
different than C where a variable name refers to a memory location in which
a value is stored (and assignment copies data into that location). 
Because of this, there is no direct way to map variable 
assignment in C to variable assignment in Python.
</p>

<p>
To provide access to C global variables, SWIG creates a special
object called `<tt>cvar</tt>' that is added to each SWIG generated
module. Global variables are then accessed as attributes of this object.
For example, consider this interface
</p>

<div class="code"><pre>
// SWIG interface file with global variables
%module example
...
%inline %{
extern int My_variable;
extern double density;
%}
...
</pre></div>
<p>
Now look at the Python interface:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; # Print out value of a C global variable
&gt;&gt;&gt; print example.cvar.My_variable
4
&gt;&gt;&gt; # Set the value of a C global variable
&gt;&gt;&gt; example.cvar.density = 0.8442
&gt;&gt;&gt; # Use in a math operation
&gt;&gt;&gt; example.cvar.density = example.cvar.density*1.10
</pre></div>

<p>
If you make an error in variable assignment, you will receive an
error message.  For example:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; example.cvar.density = "Hello"
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
TypeError: C variable 'density (double )'
&gt;&gt;&gt; 
</pre></div>

<p>
If a variable is declared as <tt>const</tt>, it is wrapped as a
read-only variable.  Attempts to modify its value will result in an
error.
</p>

<p>
To make ordinary variables read-only, you can use the <tt>%immutable</tt> directive. For example:
</p>

<div class="code">
<pre>
%{
extern char *path;
%}
%immutable;
extern char *path;
%mutable;
</pre>
</div>

<p>
The <tt>%immutable</tt> directive stays in effect until it is explicitly disabled or cleared using
<tt>%mutable</tt>.
See the <a href="SWIG.html#SWIG_readonly_variables">Creatng read-only variables</a> section for further details.
</p>

<p>
If you just want to make a specific variable immutable, supply a declaration name.  For example:
</p>

<div class="code">
<pre>
%{
extern char *path;
%}
%immutable path;
...
extern char *path;      // Read-only (due to %immutable)
</pre>
</div>

<p>
If you would like to access variables using a name other than "<tt>cvar</tt>", it can be
changed using the <tt>-globals</tt> option :
</p>

<div class="shell"><pre>
$ swig -python -globals myvar example.i
</pre></div>

<p>
Some care is in order when importing multiple SWIG modules.
If you use the "<tt>from &lt;file&gt; import *</tt>" style of
importing, you will get a name clash on the variable `<tt>cvar</tt>'
and you will only be able to access global variables from the last
module loaded. To prevent this, you might consider renaming
<tt>cvar</tt> or making it private to the module by giving it a name
that starts with a leading underscore. SWIG does not create <tt>cvar</tt>
if there are no global variables in a module.
</p>

<H3><a name="Python_nn17"></a>26.3.4 Constants and enums</H3>


<p>
C/C++ constants are installed as Python objects containing the
appropriate value.  To create a constant, use <tt>#define</tt>, <tt>enum</tt>, or the
<tt>%constant</tt> directive.  For example:
</p>

<div class="code">
<pre>
#define PI 3.14159
#define VERSION "1.0"

enum Beverage { ALE, LAGER, STOUT, PILSNER };

%constant int FOO = 42;
%constant const char *path = "/usr/local";
</pre>
</div>

<p>
For enums, make sure that the definition of the enumeration actually appears in a header
file or in the wrapper file somehow---if you just stick an enum in a SWIG interface without
also telling the C compiler about it, the wrapper code won't compile.
</p>

<p>
Note:  declarations declared as <tt>const</tt> are wrapped as read-only variables and
will be accessed using the <tt>cvar</tt> object described in the previous section.  They
are not wrapped as constants.   For further discussion about this, see the <a href="SWIG.html#SWIG">SWIG Basics</a> chapter.
</p>

<p>
Constants are not guaranteed to remain constant in Python---the name
of the constant could be accidentally reassigned to refer to some
other object.  Unfortunately, there is no easy way for SWIG to
generate code that prevents this.  You will just have to be careful.
</p>

<H3><a name="Python_nn18"></a>26.3.5 Pointers</H3>


<p>
C/C++ pointers are fully supported by SWIG.  Furthermore, SWIG has no
problem working with incomplete type information.  Here is a rather
simple interface:
</p>

<div class="code">
<pre>
%module example

FILE *fopen(const char *filename, const char *mode);
int fputs(const char *, FILE *);
int fclose(FILE *);
</pre>
</div>

<p>
When wrapped, you will be able to use the functions in a natural way from Python. For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; f = example.fopen("junk","w")
&gt;&gt;&gt; example.fputs("Hello World\n", f)
&gt;&gt;&gt; example.fclose(f)
</pre>
</div>

<p>
If this makes you uneasy, rest assured that there is no
deep magic involved.  Underneath the covers, pointers to C/C++ objects are
simply represented as opaque values using an especial python container object:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; print f
&lt;Swig Object at _08a71808_p_FILE&gt;
</pre></div>

<p>
This pointer value can be freely passed around to different C functions that
expect to receive an object of type <tt>FILE *</tt>.  The only thing you can't do is 
dereference the pointer from Python. Of course, that isn't much of a concern in this example.
</p>

<p>
In older versions of Swig (1.3.22 or older), pointers were represented
using a plain string object. If you have an old package that still
requires that representation, or you just feel nostalgic, you can
always retreive it by casting the pointer object to a string:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; print str(f)
_c0671108_p_FILE
</pre></div>

<p>
Also, if you need to pass the raw pointer value to some external
python library, you can do it by casting the pointer object to an
integer:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; print int(f)
135833352
</pre></div>

<p>
However, the inverse operation is not possible, i.e., you can't build
a Swig pointer object from a raw integer value.
</p>

<p>
Note also that the '0' or NULL pointer is always represented by
<tt>None</tt>, no matter what type swig is addressing. In the
previous example, you can call:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; example.fclose(None)
</pre>
</div>

<p>
and that will be equivalent to the following, but not really useful, C
code:
</p>

<div class="code">
<pre>
FILE *f = NULL;
fclose(f);
</pre>
</div>

<p>
As much as you might be inclined to modify a pointer value directly
from Python, don't.  The hexadecimal encoding is not necessarily the
same as the logical memory address of the underlying object.  Instead
it is the raw byte encoding of the pointer value.  The encoding will
vary depending on the native byte-ordering of the platform (i.e.,
big-endian vs. little-endian).  Similarly, don't try to manually cast
a pointer to a new type by simply replacing the type-string.  This may
not work like you expect, it is particularly dangerous when casting
C++ objects.  If you need to cast a pointer or change its value,
consider writing some helper functions instead.  For example:
</p>

<div class="code">
<pre>
%inline %{
/* C-style cast */
Bar *FooToBar(Foo *f) {
   return (Bar *) f;
}

/* C++-style cast */
Foo *BarToFoo(Bar *b) {
   return dynamic_cast&lt;Foo*&gt;(b);
}

Foo *IncrFoo(Foo *f, int i) {
    return f+i;
}
%}
</pre>
</div>

<p>
Also, if working with C++, you should always try
to use the new C++ style casts.  For example, in the above code, the
C-style cast may return a bogus result whereas as the C++-style cast will return
<tt>None</tt> if the conversion can't be performed.
</p>

<H3><a name="Python_nn19"></a>26.3.6 Structures</H3>


<p>
If you wrap a C structure, it is wrapped by a Python class.  This provides
a very natural interface.  For example,
</p>

<div class="code"><pre>
struct Vector {
	double x,y,z;
};

</pre></div>

<p>
is used as follows:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; v = example.Vector()
&gt;&gt;&gt; v.x = 3.5
&gt;&gt;&gt; v.y = 7.2
&gt;&gt;&gt; print v.x, v.y, v.z
7.8 -4.5 0.0
&gt;&gt;&gt; 
</pre></div>

<p>
Similar access is provided for unions and the data members of C++ classes.
</p>

<p>
If you print out the value of <tt>v</tt> in the above example, you will see
something like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; print v
&lt;C Vector instance at _18e31408_p_Vector&gt;
</pre>
</div>

<p>
This object is actually a Python instance that has been wrapped around a pointer to the low-level
C structure.  This instance doesn't actually do anything--it just serves as a proxy.
The pointer to the C object can be found in the the <tt>.this</tt>
attribute.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; print v.this
_18e31408_p_Vector
&gt;&gt;&gt;
</pre>
</div>

<p>
Further details about the Python proxy class are covered a little later.
</p>

<p>
<tt>const</tt> members of a structure are read-only. Data members
can also be forced to be read-only using the <tt>%immutable</tt> directive. For example:
</p>

<div class="code">
<pre>
struct Foo {
   ...
   %immutable;
   int x;        /* Read-only members */
   char *name;
   %mutable;
   ...
};
</pre>
</div>

<p>
When <tt>char *</tt> members of a structure are wrapped, the contents are assumed to be
dynamically allocated using <tt>malloc</tt> or <tt>new</tt> (depending on whether or not
SWIG is run with the -c++ option).   When the structure member is set, the old contents will be 
released and a new value created.   If this is not the behavior you want, you will have to use
a typemap (described later).
</p>

<p>
If a structure contains arrays, access to those arrays is managed through pointers.  For
example, consider this:
</p>

<div class="code">
<pre>
struct Bar {
    int  x[16];
};
</pre>
</div>

<p>
If accessed in Python, you will see behavior like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; b = example.Bar()
&gt;&gt;&gt; print b.x
_801861a4_p_int
&gt;&gt;&gt; 
</pre>
</div>

<p>
This pointer can be passed around to functions that expect to receive
an <tt>int *</tt> (just like C).   You can also set the value of an array member using
another pointer.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; c = example.Bar()
&gt;&gt;&gt; c.x = b.x             # Copy contents of b.x to c.x
</pre>
</div>

<p>
For array assignment, SWIG copies the entire contents of the array starting with the data pointed
to by <tt>b.x</tt>.   In this example, 16 integers would be copied.  Like C, SWIG makes
no assumptions about bounds checking---if you pass a bad pointer, you may get a segmentation
fault or access violation.
</p>

<p>
When a member of a structure is itself a structure, it is handled as a
pointer.  For example, suppose you have two structures like this:
</p>

<div class="code">
<pre>
struct Foo {
   int a;
};

struct Bar {
   Foo f;
};
</pre>
</div>

<p>
Now, suppose that you access the <tt>f</tt> attribute of <tt>Bar</tt> like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; b = Bar()
&gt;&gt;&gt; x = b.f
</pre>
</div>

<p>
In this case, <tt>x</tt> is a pointer that points to the <tt>Foo</tt> that is inside <tt>b</tt>.
This is the same value as generated by this C code:
</p>

<div class="code">
<pre>
Bar b;
Foo *x = &amp;b-&gt;f;       /* Points inside b */
</pre>
</div>

<p>
Because the pointer points inside the structure, you can modify the contents and 
everything works just like you would expect. For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; b = Bar()
&gt;&gt;&gt; b.f.a = 3               # Modify attribute of structure member
&gt;&gt;&gt; x = b.f                   
&gt;&gt;&gt; x.a = 3                 # Modifies the same structure
</pre>
</div>

<H3><a name="Python_nn20"></a>26.3.7 C++ classes</H3>


<p>
C++ classes are wrapped by Python classes as well. For example, if you have this class,
</p>

<div class="code"><pre>
class List {
public:
  List();
  ~List();
  int  search(char *item);
  void insert(char *item);
  void remove(char *item);
  char *get(int n);
  int  length;
};
</pre></div>

<p>
you can use it in Python like this:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; l = example.List()
&gt;&gt;&gt; l.insert("Ale")
&gt;&gt;&gt; l.insert("Stout")
&gt;&gt;&gt; l.insert("Lager")
&gt;&gt;&gt; l.get(1)
'Stout'
&gt;&gt;&gt; print l.length
3
&gt;&gt;&gt;
</pre></div>

<p>
Class data members are accessed in the same manner as C structures.  
</p>

<p>
Static class members present a special problem for Python.  Prior to Python-2.2, 
Python classes had no support for static methods and no version of Python
supports static member variables in a manner that SWIG can utilize.  Therefore, 
SWIG generates wrappers that try to work around some of these issues.  To illustrate,
suppose you have a class like this:
</p>

<div class="code">
<pre>
class Spam {
public:
   static void foo();
   static int bar;

};
</pre>
</div>

<p>
In Python, the static member can be access in three different ways:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; example.Spam_foo()    # Spam::foo()
&gt;&gt;&gt; s = example.Spam()
&gt;&gt;&gt; s.foo()               # Spam::foo() via an instance
&gt;&gt;&gt; example.Spam.foo()    # Spam::foo(). Python-2.2 only
</pre>
</div>

<p>
The first two methods of access are supported in all versions of Python.  The
last technique is only available in Python-2.2 and later versions.
</p>

<p>
Static member variables are currently accessed as global variables.  This means,
they are accessed through <tt>cvar</tt> like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; print example.cvar.Spam_bar
7
</pre>
</div>

<H3><a name="Python_nn21"></a>26.3.8 C++ inheritance</H3>


<p>
SWIG is fully aware of issues related to C++ inheritance.  Therefore, if you have
classes like this
</p>

<div class="code">
<pre>
class Foo {
...
};

class Bar : public Foo {
...
};
</pre>
</div>

<p>
those classes are wrapped into a hierarchy of Python classes that reflect the same inheritance
structure.   All of the usual Python utility functions work normally:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; b = Bar()
&gt;&gt;&gt; instance(b,Foo)
1
&gt;&gt;&gt; issubclass(Bar,Foo)
1
&gt;&gt;&gt; issubclass(Foo,Bar)
0
</pre>
</div>

<p>
Furthermore, if you have functions like this
</p>

<div class="code">
<pre>
void spam(Foo *f);
</pre>
</div>

<p>
then the function <tt>spam()</tt> accepts <tt>Foo *</tt> or a pointer to any class derived from <tt>Foo</tt>.
</p>

<p>
It is safe to use multiple inheritance with SWIG.
</p>

<H3><a name="Python_nn22"></a>26.3.9 Pointers, references, values, and arrays</H3>


<p>
In C++, there are many different ways a function might receive
and manipulate objects.  For example:
</p>

<div class="code">
<pre>
void spam1(Foo *x);      // Pass by pointer
void spam2(Foo &amp;x);      // Pass by reference
void spam3(const Foo &amp;x);// Pass by const reference
void spam4(Foo x);       // Pass by value
void spam5(Foo x[]);     // Array of objects
</pre>
</div>

<p>
In Python, there is no detailed distinction like this--specifically,
there are only "objects".  There are no pointers, references, arrays,
and so forth.  Because of this, SWIG unifies all of these types
together in the wrapper code.  For instance, if you actually had the
above functions, it is perfectly legal to do this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = Foo()           # Create a Foo
&gt;&gt;&gt; spam1(f)            # Ok. Pointer
&gt;&gt;&gt; spam2(f)            # Ok. Reference
&gt;&gt;&gt; spam3(f)            # Ok. Const reference
&gt;&gt;&gt; spam4(f)            # Ok. Value.
&gt;&gt;&gt; spam5(f)            # Ok. Array (1 element)
</pre>
</div>

<p>
Similar behavior occurs for return values.  For example, if you had
functions like this,
</p>

<div class="code">
<pre>
Foo *spam6();
Foo &amp;spam7();
Foo  spam8();
const Foo &amp;spam9();
</pre>
</div>

<p>
then all three functions will return a pointer to some <tt>Foo</tt> object.
Since the third function (spam8) returns a value, newly allocated memory is used 
to hold the result and a pointer is returned (Python will release this memory 
when the return value is garbage collected). The fourth case (spam9)
which returns a const reference, in most of the cases will be 
treated as a returning value, and it will follow the same
allocation/deallocation process.
</p>

<H3><a name="Python_nn23"></a>26.3.10 C++ overloaded functions</H3>


<p>
C++ overloaded functions, methods, and constructors are mostly supported by SWIG.  For example,
if you have two functions like this:
</p>

<div class="code">
<pre>
void foo(int);
void foo(char *c);
</pre>
</div>

<p>
You can use them in Python in a straightforward manner:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; foo(3)           # foo(int)
&gt;&gt;&gt; foo("Hello")     # foo(char *c)
</pre>
</div>

<p>
Similarly, if you have a class like this,
</p>

<div class="code">
<pre>
class Foo {
public:
    Foo();
    Foo(const Foo &amp;);
    ...
};
</pre>
</div>

<p>
you can write Python code like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = Foo()          # Create a Foo
&gt;&gt;&gt; g = Foo(f)         # Copy f
</pre>
</div>

<p>
Overloading support is not quite as flexible as in C++. Sometimes there are methods that SWIG
can't disambiguate. For example:
</p>

<div class="code">
<pre>
void spam(int);
void spam(short);
</pre>
</div>

<p>
or
</p>

<div class="code">
<pre>
void foo(Bar *b);
void foo(Bar &amp;b);
</pre>
</div>

<p>
If declarations such as these appear, you will get a warning message like this:
</p>

<div class="shell">
<pre>
example.i:12: Warning(509): Overloaded spam(short) is shadowed by spam(int)
at example.i:11.
</pre>
</div>

<p>
To fix this, you either need to ignore or rename one of the methods.  For example:
</p>

<div class="code">
<pre>
%rename(spam_short) spam(short);
...
void spam(int);    
void spam(short);   // Accessed as spam_short
</pre>
</div>

<p>
or
</p>

<div class="code">
<pre>
%ignore spam(short);
...
void spam(int);    
void spam(short);   // Ignored
</pre>
</div>

<p>
SWIG resolves overloaded functions and methods using a disambiguation scheme that ranks and sorts
declarations according to a set of type-precedence rules.    The order in which declarations appear
in the input does not matter except in situations where ambiguity arises--in this case, the
first declaration takes precedence.
</p>

<p>
Please refer to the "SWIG and C++" chapter for more information about overloading. 
</p>

<H3><a name="Python_nn24"></a>26.3.11 C++ operators</H3>


<p>
Certain C++ overloaded operators can be handled automatically by SWIG.  For example,
consider a class like this:
</p>

<div class="code">
<pre>
class Complex {
private:
  double rpart, ipart;
public:
  Complex(double r = 0, double i = 0) : rpart(r), ipart(i) { }
  Complex(const Complex &amp;c) : rpart(c.rpart), ipart(c.ipart) { }
  Complex &amp;operator=(const Complex &amp;c);

  Complex operator+=(const Complex &amp;c) const;
  Complex operator+(const Complex &amp;c) const;
  Complex operator-(const Complex &amp;c) const;
  Complex operator*(const Complex &amp;c) const;
  Complex operator-() const;
  
  double re() const { return rpart; }
  double im() const { return ipart; }
};
</pre>
</div>

<p>
When wrapped, it works like you expect:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; c = Complex(3,4)
&gt;&gt;&gt; d = Complex(7,8)
&gt;&gt;&gt; e = c + d
&gt;&gt;&gt; e.re()
10.0
&gt;&gt;&gt; e.im()
12.0
&gt;&gt;&gt; c += d
&gt;&gt;&gt; c.re()
10.0
&gt;&gt;&gt; c.im()
12.0

</pre>
</div>

<p>
One restriction with operator overloading support is that SWIG is not
able to fully handle operators that aren't defined as part of the class.
For example, if you had code like this
</p>

<div class="code">
<pre>
class Complex {
...
friend Complex operator+(double, const Complex &amp;c);
...
};
</pre>
</div>

<p>
then SWIG ignores it and issues a warning.   You can still wrap the operator,
but you may have to encapsulate it in a special function.  For example:
</p>

<div class="code">
<pre>
%rename(Complex_add_dc) operator+(double, const Complex &amp;);
</pre>
</div>

<p>
There are ways to make this operator appear as part of the class using the <tt>%extend</tt> directive.
Keep reading.
</p>

<p>
Also, be aware that certain operators don't map cleanly to Python.  For instance,
overloaded assignment operators don't map to Python semantics and will be ignored.
</p>

<H3><a name="Python_nn25"></a>26.3.12 C++ namespaces</H3>


<p>
SWIG is aware of C++ namespaces, but namespace names do not appear in
the module nor do namespaces result in a module that is broken up into
submodules or packages.  For example, if you have a file like this,
</p>

<div class="code">
<pre>
%module example

namespace foo {
   int fact(int n);
   struct Vector {
       double x,y,z;
   };
};
</pre>
</div>

<p>
it works in Python as follows:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; example.fact(3)
6
&gt;&gt;&gt; v = example.Vector()
&gt;&gt;&gt; v.x = 3.4
&gt;&gt;&gt; print v.y
0.0
&gt;&gt;&gt;
</pre>
</div>

<p>
If your program has more than one namespace, name conflicts (if any) can be resolved using <tt>%rename</tt>
For example:
</p>

<div class="code">
<pre>
%rename(Bar_spam) Bar::spam;

namespace Foo {
    int spam();
}

namespace Bar {
    int spam();
}
</pre>
</div>

<p>
If you have more than one namespace and your want to keep their
symbols separate, consider wrapping them as separate SWIG modules.
For example, make the module name the same as the namespace and create
extension modules for each namespace separately.  If your program
utilizes thousands of small deeply nested namespaces each with
identical symbol names, well, then you get what you deserve.
</p>

<H3><a name="Python_nn26"></a>26.3.13 C++ templates</H3>


<p>
C++ templates don't present a huge problem for SWIG.  However, in order
to create wrappers, you have to tell SWIG to create wrappers for a particular
template instantiation.  To do this, you use the <tt>%template</tt> directive.
For example:
</p>

<div class="code">
<pre>
%module example
%{
#include "pair.h"
%}

template&lt;class T1, class T2&gt;
struct pair {
   typedef T1 first_type;
   typedef T2 second_type;
   T1 first;
   T2 second;
   pair();
   pair(const T1&amp;, const T2&amp;);
  ~pair();
};

%template(pairii) pair&lt;int,int&gt;;
</pre>
</div>

<p>
In Python:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; p = example.pairii(3,4)
&gt;&gt;&gt; p.first
3
&gt;&gt;&gt; p.second
4
</pre>
</div>

<p>
Obviously, there is more to template wrapping than shown in this example.
More details can be found in the <a href="SWIGPlus.html#SWIGPlus">SWIG and C++</a> chapter.   Some more complicated
examples will appear later.
</p>

<H3><a name="Python_nn27"></a>26.3.14 C++ Smart Pointers</H3>


<p>
In certain C++ programs, it is common to use classes that have been wrapped by
so-called "smart pointers."   Generally, this involves the use of a template class
that implements <tt>operator-&gt;()</tt> like this:
</p>

<div class="code">
<pre>
template&lt;class T&gt; class SmartPtr {
   ...
   T *operator-&gt;();
   ...
}
</pre>
</div>

<p>
Then, if you have a class like this,
</p>

<div class="code">
<pre>
class Foo {
public:
     int x;
     int bar();
};
</pre>
</div>

<p>
A smart pointer would be used in C++ as follows:
</p>

<div class="code">
<pre>
SmartPtr&lt;Foo&gt; p = CreateFoo();   // Created somehow (not shown)
...
p-&gt;x = 3;                        // Foo::x
int y = p-&gt;bar();                // Foo::bar
</pre>
</div>

<p>
To wrap this in Python, simply tell SWIG about the <tt>SmartPtr</tt> class and the low-level
<tt>Foo</tt> object.  Make sure you instantiate <tt>SmartPtr</tt> using <tt>%template</tt> if necessary.
For example:
</p>

<div class="code">
<pre>
%module example
...
%template(SmartPtrFoo) SmartPtr&lt;Foo&gt;;
...
</pre>
</div>

<p>
Now, in Python, everything should just "work":
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; p = example.CreateFoo()          # Create a smart-pointer somehow
&gt;&gt;&gt; p.x = 3                          # Foo::x
&gt;&gt;&gt; p.bar()                          # Foo::bar
</pre>
</div>

<p>
If you ever need to access the underlying pointer returned by <tt>operator-&gt;()</tt> itself,
simply use the <tt>__deref__()</tt> method.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = p.__deref__()     # Returns underlying Foo *
</pre>
</div>


<H3><a name="Python_nn27a"></a>26.3.15 C++ Reference Counted Objects (ref/unref)</H3>


<p>
Another usual idiom in C++ is the use of reference counted
objects. Consider for example:

<div class="code">
<pre>
class RCObj  {
  // implement the ref counting mechanism
  int add_ref();
  int del_ref();
  int ref_count();

public:
  virtual ~RCObj() = 0;

  int ref() const {
    return add_ref();
  }

  int unref() const   {
    if (ref_count() == 0 || del_ref() == 0 ) {
	delete this;
	return 0;
      } 
    return ref_count();
  }
};


class A : RCObj {
public:
  A();
  int foo();
};


class B {
  A *_a;

public:
  B(A *a) : _a(a) { 
    a-&gt;ref(); 
  }

  ~B() { 
    a-&gt;unref(); 
  }
};

int main() {
  A *a  = new A();
  a->ref();           // 'a' is ref here

  B *b1 = new B(a);   // 'a' is ref here
  if (1 + 1 == 2) {
    B *b2 = new B(a); // 'a' is ref here
    delete b2;        // 'a' is unref, but not deleted   
  }

  delete b1;          // 'a' is unref, but not deleted   
  a->unref();         // 'a' is unref and deleted
}
</pre>
</div>

<p>
In the example above, the 'A' class instance 'a' is a reference counted
object, which can't be deleted arbitrarily since it is shared between
the objects 'b1' and 'b2'. 'A' is derived from an Reference Counted
Object 'RCObj', which implements the ref/unref idiom.
</p>

<p>
To tell SWIG that 'RCObj' and all its derived classes are reference
counted objects, you use the "ref" and "unref" features. For example:
</p>


<div class="code">
<pre>
%module example
...

%feature("ref")   RCObj "$this->ref();"
%feature("unref") RCObj "$this->unref();"

%include "rcobj.h"
%include "A.h"
...
</pre>
</div>

<p>
where the code passed to the "ref" and "unref" methods will be
executed as needed whenever a new object is passed to python, or when
python tries to release the shadow object instance, respectively. 
</p>

<p> 
In the python side, the use of a reference counted object is not
different than any other regular instance:
</p>

<div class="targetlang">
<pre>
def create_A():
  a = A()         # SWIG ref 'a' (new object is passed to python)
  b1 = B(a)       # C++  ref 'a'
  if 1 + 1 == 2:
     b2 = B(a)    # C++ ref 'a'
  return a        # 'b1' and 'b2' are released, C++ unref 'a' twice

a = create_A()   
exit              # 'a' is released, SWIG unref 'a'
</pre>
</div>

<p>
Note that the user doens't explicitly need to call 'a->ref()' nor 'a->unref()'
(as neither 'delete a'). Instead, SWIG take cares of executing the "ref"
and "unref" codes as needed.  If the user doesn't specify the
"ref/unref" features, SWIG will produce a code equivalent to define
them as:
</p>

<div class="code">
<pre>
%feature("ref")   ""
%feature("unref") "delete $this;"
</pre>
</div>

<p>
In other words, SWIG will not do anything special when a new object
is passed to python, and it will always 'delete' the object when
python releases the shadow instance.
</p>


<H2><a name="Python_nn28"></a>26.4 Further details on the Python class interface</H2>


<p>
In the previous section, a high-level view of Python wrapping was
presented.  A key component of this wrapping is that structures and
classes are wrapped by Python proxy classes.  This provides a very
natural Python interface and allows SWIG to support a number of
advanced features such as operator overloading.   However, a number
of low-level details were omitted.  This section provides a brief overview
of how the proxy classes work.
</p>

<H3><a name="Python_nn29"></a>26.4.1 Proxy classes</H3>


<p>
In the <a href="SWIG.html#SWIG">"SWIG basics"</a> and <a href="SWIGPlus.html#SWIGPlus">"SWIG and C++"</a> chapters,
details of low-level structure and class wrapping are described.  To summarize those chapters, if you
have a class like this
</p>

<div class="code">
<pre>
class Foo {
public:
     int x;
     int spam(int);
     ...
</pre>
</div>

<p>
then SWIG transforms it into a set of low-level procedural wrappers. For example:
</p>

<div class="code">
<pre>
Foo *new_Foo() {
    return new Foo();
}
void delete_Foo(Foo *f) {
    delete f;
}
int Foo_x_get(Foo *f) {
    return f-&gt;x;
}
void Foo_x_set(Foo *f, int value) {
    f-&gt;x = value;
}
int Foo_spam(Foo *f, int arg1) {
    return f-&gt;spam(arg1);
}
</pre>
</div>

<p>
These wrappers can be found in the low-level extension module (e.g., <tt>_example</tt>).
</p>

<p>
Using these wrappers, SWIG generates a high-level Python proxy class (also known as a shadow class) like this (shown 
for Python 2.2):
</p>

<div class="targetlang">
<pre>
import _example

class Foo(object):
     def __init__(self):
         self.this = _example.new_Foo()
         self.thisown = 1
     def __del__(self):
         if self.thisown:
               _example.delete_Foo(self.this)
     def spam(self,arg1):
         return _example.Foo_spam(self.this,arg1)
     x = property(_example.Foo_x_get, _example.Foo_x_set)
</pre>
</div>

<p>
This class merely holds a pointer to the underlying C++ object (<tt>.this</tt>) and dispatches methods and 
member variable access to that object using the low-level accessor functions.   From a user's point of
view, it makes the class work normally:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = example.Foo()
&gt;&gt;&gt; f.x = 3
&gt;&gt;&gt; y = f.spam(5)
</pre>
</div>

<p>
The fact that the class has been wrapped by a real Python class offers certain advantages.  For instance,
you can attach new Python methods to the class and you can even inherit from it (something not supported
by Python built-in types until Python 2.2).
</p>

<H3><a name="Python_nn30"></a>26.4.2 Memory management</H3>


<p>
Associated with proxy object, is an ownership flag <tt>.thisown</tt>   The value of this
flag determines who is responsible for deleting the underlying C++ object.   If set to 1,
the Python interpreter will destroy the C++ object when the proxy class is 
garbage collected.   If set to 0 (or if the attribute is missing), then the destruction
of the proxy class has no effect on the C++ object.
</p>

<p>
When an object is created by a constructor or returned by value, Python automatically takes
ownership of the result.  For example:
</p>

<div class="code">
<pre>
class Foo {
public:
    Foo();
    Foo bar();
};
</pre>
</div>

<p>
In Python:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = Foo()
&gt;&gt;&gt; f.thisown
1
&gt;&gt;&gt; g = f.bar()
&gt;&gt;&gt; g.thisown
1
</pre>
</div>

<p>
On the other hand, when pointers are returned to Python, there is often no way to know where
they came from.  Therefore, the ownership is set to zero.  For example:
</p>

<div class="code">
<pre>
class Foo {
public:
    ...
    Foo *spam();
    ...
};
</pre>
</div>

<br>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = Foo()
&gt;&gt;&gt; s = f.spam()
&gt;&gt;&gt; print s.thisown
0
&gt;&gt;&gt;
</pre>
</div>

<p>
This behavior is especially important for classes that act as
containers.  For example, if a method returns a pointer to an object
that is contained inside another object, you definitely don't want
Python to assume ownership and destroy it!
</p>

<p>
Related to containers, ownership issues can arise whenever an object is assigned to a member
or global variable.  For example, consider this interface:
</p>

<div class="code">
<pre>
%module example

struct Foo {
    int  value;
    Foo  *next;
};

Foo *head = 0;
</pre>
</div>

<p>
When wrapped in Python, careful observation will reveal that ownership changes whenever an object
is assigned to a global variable.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = example.Foo()
&gt;&gt;&gt; f.thisown
1
&gt;&gt;&gt; example.cvar.head = f           
&gt;&gt;&gt; f.thisown
0
&gt;&gt;&gt;
</pre>
</div>

<p>
In this case, C is now holding a reference to the object---you probably don't want Python to destroy it.
Similarly, this occurs for members.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; f = example.Foo()
&gt;&gt;&gt; g = example.Foo()
&gt;&gt;&gt; f.thisown
1
&gt;&gt;&gt; g.thisown
1
&gt;&gt;&gt; f.next = g
&gt;&gt;&gt; g.thisown
0
&gt;&gt;&gt;
</pre>
</div>

<p>
For the most part, memory management issues remain hidden.  However,
there are occasionally situations where you might have to manually
change the ownership of an object.  For instance, consider code like this:
</p>

<div class="code">
<pre>
class Node {
   Object *value;
public:
   void set_value(Object *v) { value = v; }
   ...
};
</pre>
</div>

<p>
Now, consider the following Python code:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; v = Object()           # Create an object
&gt;&gt;&gt; n = Node()             # Create a node
&gt;&gt;&gt; n.set_value(v)         # Set value
&gt;&gt;&gt; v.thisown
1
&gt;&gt;&gt; del v
</pre>
</div>

<p>
In this case, the object <tt>n</tt> is holding a reference to
<tt>v</tt> internally.  However, SWIG has no way to know that this
has occurred.  Therefore, Python still thinks that it has ownership of the
object.  Should the proxy object be destroyed, then the C++ destructor
will be invoked and <tt>n</tt> will be holding a stale-pointer.  If
you're lucky, you will only get a segmentation fault.
</p>

<p>
To work around this, it is always possible to flip the ownership flag. For example,
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; v.thisown = 0
</pre>
</div>

<p>
It is also possible to deal with situations like this using
typemaps--an advanced topic discussed later.
</p>

<H3><a name="Python_nn31"></a>26.4.3 Python 2.2 and classic classes</H3>


<p>
SWIG makes every attempt to preserve backwards compatibility with
older versions of Python to the extent that it is possible.  However,
in Python-2.2, an entirely new type of class system was introduced.
This new-style class system offers many enhancements including static
member functions, properties (managed attributes), and class methods.
Details about all of these changes can be found on <a
href="//www.python.org">www.python.org</a> and is not repeated here.
</p>

<p>
To address differences between Python versions, SWIG currently emits
dual-mode proxy class wrappers.  In Python-2.2 and newer releases,
these wrappers encapsulate C++ objects in new-style classes that take
advantage of new features (static methods and properties).  However,
if these very same wrappers are imported into an older version of Python, 
old-style classes are used instead.
</p>

<p>
This dual-nature of the wrapper code means that you can create extension
modules with SWIG and those modules will work with all versions of Python
ranging from Python-1.4 to the very latest release.  Moreover, the wrappers take
advantage of Python-2.2 features when available.
</p>

<p>
For the most part, the interface presented to users is the same regardless
of what version of Python is used.  The only incompatibility lies in the handling
of static member functions.  In Python-2.2, they can be accessed via the
class itself.  In Python-2.1 and earlier, they have to be accessed as a global
function or through an instance (see the earlier section).
</p>

<H2><a name="directors"></a>26.5 Cross language polymorphism</H2>


<p>
Proxy classes provide a more natural, object-oriented way to access
extension classes. As described above, each proxy instance has an
associated C++ instance, and method calls to the proxy are passed to the
C++ instance transparently via C wrapper functions.
</p>

<p>
This arrangement is asymmetric in the sense that no corresponding
mechanism exists to pass method calls down the inheritance chain from
C++ to Python. In particular, if a C++ class has been extended in Python
(by extending the proxy class), these extensions will not be visible
from C++ code. Virtual method calls from C++ are thus not able access
the lowest implementation in the inheritance chain.
</p>

<p>
Changes have been made to SWIG 1.3.18 to address this problem and
make the relationship between C++ classes and proxy classes more
symmetric. To achieve this goal, new classes called directors are
introduced at the bottom of the C++ inheritance chain. The job of the
directors is to route method calls correctly, either to C++
implementations higher in the inheritance chain or to Python
implementations lower in the inheritance chain. The upshot is that C++
classes can be extended in Python and from C++ these extensions look
exactly like native C++ classes. Neither C++ code nor Python code needs
to know where a particular method is implemented: the combination of
proxy classes, director classes, and C wrapper functions takes care of
all the cross-language method routing transparently.
</p>

<H3><a name="Python_nn33"></a>26.5.1 Enabling directors</H3>


<p>
The director feature is disabled by default.  To use directors you
must make two changes to the interface file.  First, add the "directors"
option to the %module directive, like this:
</p>

<div class="code">
<pre>
%module(directors="1") modulename
</pre>
</div>

<p>
Without this option no director code will be generated.  Second, you
must use the %feature("director") directive to tell SWIG which classes 
and methods should get directors.  The %feature directive can be applied 
globally, to specific classes, and to specific methods, like this:
</p>

<div class="code">
<pre>
// generate directors for all classes that have virtual methods
%feature("director");         

// genarate directors for all virtual methods in class Foo
%feature("director") Foo;      

// generate a director for just Foo::bar()
%feature("director") Foo::bar; 
</pre>
</div>

<p>
You can use the %feature("nodirector") directive to turn off
directors for specific classes or methods.  So for example,
</p>

<div class="code">
<pre>
%feature("director") Foo;
%feature("nodirector") Foo::bar;
</pre>
</div>

<p>
will generate directors for all virtual methods of class Foo except
bar().  
</p>

<p>
Directors can also be generated implicitly through inheritance. 
In the following, class Bar will get a director class that handles
the methods one() and two() (but not three()):
</p>

<div class="code">
<pre>
%feature("director") Foo;
class Foo {
public:
    Foo(int foo);
    virtual void one();
    virtual void two();
};

class Bar: public Foo {
public:
    virtual void three();
};
</pre>
</div>

<p>
then at the python side you can define
</p>

<div class="targetlang">
<pre>
import mymodule

class MyFoo(mymodule.Foo):
  def __init__(self, foo):
     mymodule.Foo(self, foo)  

  def one(self):
     print "one from python"
</pre>
</div>


<H3><a name="Python_nn34"></a>26.5.2 Director classes</H3>


<p>
For each class that has directors enabled, SWIG generates a new class
that derives from both the class in question and a special
<tt>Swig::Director</tt> class. These new classes, referred to as director
classes, can be loosely thought of as the C++ equivalent of the Python
proxy classes. The director classes store a pointer to their underlying
Python object and handle various issues related to object ownership.
Indeed, this is quite similar to the "this" and "thisown" members of the
Python proxy classes.
</p>

<p>
For simplicity let's ignore the <tt>Swig::Director</tt> class and refer to the
original C++ class as the director's base class. By default, a director
class extends all virtual methods in the inheritance chain of its base
class (see the preceding section for how to modify this behavior).
Thus all virtual method calls, whether they originate in C++ or in
Python via proxy classes, eventually end up in at the implementation in
the director class. The job of the director methods is to route these
method calls to the appropriate place in the inheritance chain. By
"appropriate place" we mean the method that would have been called if
the C++ base class and its extensions in Python were seamlessly
integrated. That seamless integration is exactly what the director
classes provide, transparently skipping over all the messy extension API
glue that binds the two languages together.
</p>

<p>
In reality, the "appropriate place" is one of only two possibilities:
C++ or Python. Once this decision is made, the rest is fairly easy. If
the correct implementation is in C++, then the lowest implementation of
the method in the C++ inheritance chain is called explicitly. If the
correct implementation is in Python, the Python API is used to call the
method of the underlying Python object (after which the usual virtual
method resolution in Python automatically finds the right
implementation).
</p>

<p>
Now how does the director decide which language should handle the method call?
The basic rule is to handle the method in Python, unless there's a good
reason not to. The reason for this is simple: Python has the most
"extended" implementation of the method. This assertion is guaranteed,
since at a minimum the Python proxy class implements the method. If the
method in question has been extended by a class derived from the proxy
class, that extended implementation will execute exactly as it should.
If not, the proxy class will route the method call into a C wrapper
function, expecting that the method will be resolved in C++. The wrapper
will call the virtual method of the C++ instance, and since the director
extends this the call will end up right back in the director method. Now
comes the "good reason not to" part. If the director method were to blindly
call the Python method again, it would get stuck in an infinite loop. We avoid this
situation by adding special code to the C wrapper function that tells
the director method to not do this. The C wrapper function compares the
pointer to the Python object that called the wrapper function to the
pointer stored by the director. If these are the same, then the C
wrapper function tells the director to resolve the method by calling up
the C++ inheritance chain, preventing an infinite loop.
</p>

<p>
One more point needs to be made about the relationship between director
classes and proxy classes. When a proxy class instance is created in
Python, SWIG creates an instance of the original C++ class and assigns
it to <tt>.this</tt>. This is exactly what happens without directors and
is true even if directors are enabled for the particular class in
question. When a class <i>derived</i> from a proxy class is created,
however, SWIG then creates an instance of the corresponding C++ director
class. The reason for this difference is that user-defined subclasses
may override or extend methods of the original class, so the director
class is needed to route calls to these methods correctly. For
unmodified proxy classes, all methods are ultimately implemented in C++
so there is no need for the extra overhead involved with routing the
calls through Python.
</p>

<H3><a name="Python_nn35"></a>26.5.3 Ownership and object destruction</H3>


<p>
Memory management issues are slightly more complicated with directors
than for proxy classes alone. Python instances hold a pointer to the
associated C++ director object, and the director in turn holds a pointer
back to the Python object. By default, proxy classes own their C++
director object and take care of deleting it when they are garbage
collected.
</p>

<p>
This relationship can be reversed by calling the special
<tt>__disown__()</tt> method of the proxy class. After calling this
method, the <tt>.thisown</tt> flag is set to zero, and the director
class increments the reference count of the Python object. When the
director class is deleted it decrements the reference count. Assuming no
outstanding references to the Python object remain, the Python object
will be destroyed at the same time. This is a good thing, since
directors and proxies refer to each other and so must be created and
destroyed together. Destroying one without destroying the other will
likely cause your program to segfault.
</p>

<p>
To help ensure that no references to the Python object remain after
calling <tt>__disown__()</tt>, this method returns a weak reference to
the Python object. Weak references are only available in Python versions
2.1 and higher, so for older versions you must exclicitly delete all
references. Here is an example:
</p>

<div class="code">
<pre>
class Foo {
public:
    ...
};
class FooContainer {
public:
    void addFoo(Foo *);
    ...
};
</pre>
</div>

<br>

<div class="targetlang">
<pre>
&gt;&gt;&gt; c = FooContainer()
&gt;&gt;&gt; a = Foo().__disown()__
&gt;&gt;&gt; c.addFoo(a)
&gt;&gt;&gt; b = Foo()
&gt;&gt;&gt; b = b.__disown()__
&gt;&gt;&gt; c.addFoo(b)
&gt;&gt;&gt; c.addFoo(Foo().__disown()__)
</pre>
</div>

<p>
In this example, we are assuming that FooContainer will take care of
deleting all the Foo pointers it contains at some point.  Note that no hard
references to the Foo objects remain in Python.
</p>

<H3><a name="Python_nn36"></a>26.5.4 Exception unrolling</H3>


<p>
With directors routing method calls to Python, and proxies routing them
to C++, the handling of exceptions is an important concern. By default, the
directors ignore exceptions that occur during method calls that are
resolved in Python. To handle such exceptions correctly, it is necessary
to temporarily translate them into C++ exceptions. This can be done with
the %feature("director:except") directive. The following code should
suffice in most cases:
</p>

<div class="code">
<pre>
%feature("director:except") {
    if ($error != NULL) {
        throw Swig::DirectorMethodException();
    }
}
</pre>
</div>

<p>
This code will check the Python error state after each method call from
a director into Python, and throw a C++ exception if an error occured.
This exception can be caught in C++ to implement an error handler.
Currently no information about the Python error is stored in the
Swig::DirectorMethodException object, but this will likely change in
the future.
</p>

<p>
It may be the case that a method call originates in Python, travels up
to C++ through a proxy class, and then back into Python via a director
method. If an exception occurs in Python at this point, it would be nice
for that exception to find its way back to the original caller. This can
be done by combining a normal %exception directive with the
<tt>director:except</tt> handler shown above. Here is an example of a
suitable exception handler:
</p>

<div class="code">
<pre>
%exception {
    try { $action }
    catch (Swig::DirectorException &amp;e) { SWIG_fail; }
}
</pre>
</div>

<p>
The class Swig::DirectorException used in this example is actually a
base class of Swig::DirectorMethodException, so it will trap this
exception. Because the Python error state is still set when
Swig::DirectorMethodException is thrown, Python will register the
exception as soon as the C wrapper function returns.
</p>

<H3><a name="Python_nn37"></a>26.5.5 Overhead and code bloat</H3>


<p>
Enabling directors for a class will generate a new director method for
every virtual method in the class' inheritance chain. This alone can
generate a lot of code bloat for large hierarchies. Method arguments
that require complex conversions to and from target language types can
result in large director methods. For this reason it is recommended that
you selectively enable directors only for specific classes that are
likely to be extended in Python and used in C++.
</p>

<p>
Compared to classes that do not use directors, the call routing in the
director methods does add some overhead. In particular, at least one
dynamic cast and one extra function call occurs per method call from
Python. Relative to the speed of Python execution this is probably
completely negligible. For worst case routing, a method call that
ultimately resolves in C++ may take one extra detour through Python in
order to ensure that the method does not have an extended Python
implementation. This could result in a noticible overhead in some cases.
</p>

<p>
Although directors make it natural to mix native C++ objects with Python
objects (as director objects) via a common base class pointer, one
should be aware of the obvious fact that method calls to Python objects
will be much slower than calls to C++ objects. This situation can be
optimized by selectively enabling director methods (using the %feature
directive) for only those methods that are likely to be extended in
Python.
</p>

<H3><a name="Python_nn38"></a>26.5.6 Typemaps</H3>


<p>
Typemaps for input and output of most of the basic types from director
classes have been written. These are roughly the reverse of the usual
input and output typemaps used by the wrapper code. The typemap
operation names are 'directorin', 'directorout', and 'directorargout'.
The director code does not currently use any of the other kinds of typemaps.
It is not clear at this point which kinds are appropriate and
need to be supported.
</p>


<H3><a name="Python_nn39"></a>26.5.7 Miscellaneous</H3>


<p>
Director typemaps for STL classes are in place, and hence you should
be able to use std::vector, std::string, etc., as you would any other type.
</p>

<p>
<b>Note:</b> The director typemaps for return types based in const
references, such as 

<div class="code">
<pre>
class Foo {
&hellip;
    virtual const int&amp; bar();
&hellip;
};
</pre>
</div>

<p>
will work only for simple call scenarios. Usually the resulting code
is neither thread or reentrant safe. Hence, the user is advised to
avoid returning const references in director methods. For example,
the user could modify the method interface to use lvalue return
types, wherever possible, for example
</p>

<div class="code">
<pre>
class Foo {
&hellip;
    virtual int bar();
&hellip;
};
</pre>
</div>

<p>
If that is not possible, the user should avoid enabling the
director feature for reentrant, recursive or threaded member
methods that return const references.
</p>


<H2><a name="Python_nn40"></a>26.6 Common customization features</H2>


<p>
The last section presented the absolute basics of C/C++ wrapping. If
you do nothing but feed SWIG a header file, you will get an interface
that mimics the behavior described.  However, sometimes this isn't
enough to produce a nice module.  Certain types of functionality might
be missing or the interface to certain functions might be awkward.
This section describes some common SWIG features that are used to
improve your the interface to an extension module.
</p>

<H3><a name="Python_nn41"></a>26.6.1 C/C++ helper functions</H3>


<p>
Sometimes when you create a module, it is missing certain bits of functionality. For
example, if you had a function like this
</p>

<div class="code">
<pre>
void set_transform(Image *im, double m[4][4]);
</pre>
</div>

<p>
it would be accessible from Python, but there may be no easy way to call it.
For example, you might get errors like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = [
...   [1,0,0,0],
...   [0,1,0,0],
...   [0,0,1,0],
...   [0,0,0,1]]
&gt;&gt;&gt; set_transform(im,a)
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
TypeError: Type error. Expected _p_a_4__double
</pre>
</div>

<p>
The problem here is that there is no easy way to construct and manipulate a suitable
<tt>double [4][4]</tt> value to use.   To fix this, you can write some extra C helper
functions.  Just use the <tt>%inline</tt> directive. For example:
</p>

<div class="code">
<pre>
%inline %{
/* Note: double[4][4] is equivalent to a pointer to an array double (*)[4] */
double (*new_mat44())[4] {
   return (double (*)[4]) malloc(16*sizeof(double));
}
void free_mat44(double (*x)[4]) {
   free(x);
}
void mat44_set(double x[4][4], int i, int j, double v) {
   x[i][j] = v;
}
double mat44_get(double x[4][4], int i, int j) {
   return x[i][j];
}
%}
</pre>
</div>

<p>
From Python, you could then write code like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = new_mat44()
&gt;&gt;&gt; mat44_set(a,0,0,1.0)
&gt;&gt;&gt; mat44_set(a,1,1,1.0)
&gt;&gt;&gt; mat44_set(a,2,2,1.0)
...
&gt;&gt;&gt; set_transform(im,a)
&gt;&gt;&gt;
</pre>
</div>

<p>
Admittedly, this is not the most elegant looking approach.  However, it works and it wasn't too
hard to implement.  It is possible to clean this up using Python code, typemaps, and other
customization features as covered in later sections.
</p>

<H3><a name="Python_nn42"></a>26.6.2 Adding additional Python code</H3>


<p>
If writing support code in C isn't enough, it is also possible to write code in
Python.  This code gets inserted in to the <tt>.py</tt> file created by SWIG.   One
use of Python code might be to supply a high-level interface to certain functions.
For example:
</p>

<div class="code">
<pre>
void set_transform(Image *im, double x[4][4]);

...
/* Rewrite the high level interface to set_transform */
%pythoncode %{
def set_transform(im,x):
   a = new_mat44()
   for i in range(4):
       for j in range(4):
           mat44_set(a,i,j,x[i][j])
   _example.set_transform(im,a)
   free_mat44(a)
%}
</pre>
</div>

<p>
In this example, <tt>set_transform()</tt> provides a high-level Python interface built on top of
low-level helper functions.  For example, this code now seems to work:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = [
...   [1,0,0,0],
...   [0,1,0,0],
...   [0,0,1,0],
...   [0,0,0,1]]
&gt;&gt;&gt; set_transform(im,a)
&gt;&gt;&gt;
</pre>
</div>

<p>
Admittedly, this whole scheme for wrapping the two-dimension array
argument is rather ad-hoc. Besides, shouldn't a Python list or a
Numeric Python array just work normally?  We'll get to those examples
soon enough.  For now, think of this example as an illustration of
what can be done without having to rely on any of the more advanced
customization features.
</p>

<H3><a name="Python_nn43"></a>26.6.3 Class extension with %extend</H3>


<p>
One of the more interesting features of SWIG is that it can extend
structures and classes with new methods--at least in the Python interface.
Here is a simple example:
</p>

<div class="code">
<pre>
%module example
%{
#include "someheader.h"
%}

struct Vector {
   double x,y,z;
};

%extend Vector {
   char *__str__() {
       static char tmp[1024];
       sprintf(tmp,"Vector(%g,%g,%g)", self-&gt;x,self-&gt;y,self-&gt;z);
       return tmp;
   }
   Vector(double x, double y, double z) {
       Vector *v = (Vector *) malloc(sizeof(Vector));
       v-&gt;x = x;
       v-&gt;y = y;
       v-&gt;z = z;
       return v;
   }
};
</pre>
</div>

<p>
Now, in Python
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; v = example.Vector(2,3,4)
&gt;&gt;&gt; print v
Vector(2,3,4)
&gt;&gt;&gt;
</pre>
</div>

<p>
<tt>%extend</tt> can be used for many more tasks than this.  
For example, if you wanted to overload a Python operator, you might do this:
</p>

<div class="code">
<pre>
%extend Vector {
    Vector __add__(Vector *other) {
         Vector v;
         v.x = self-&gt;x + other-&gt;x;
         v.y = self-&gt;y + other-&gt;y;
         v.z = self-&gt;z + other-&gt;z;
         return v;
    }
};
</pre>
</div>

<p>
Use it like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; import example
&gt;&gt;&gt; v = example.Vector(2,3,4)
&gt;&gt;&gt; w = example.Vector(10,11,12)
&gt;&gt;&gt; print v+w
Vector(12,14,16)
&gt;&gt;&gt; 
</pre>
</div>

<p>
<tt>%extend</tt> works with both C and C++ code.  It does not modify the underlying object
in any way---the extensions only show up in the Python interface.
</p>

<H3><a name="Python_nn44"></a>26.6.4 Exception handling with %exception</H3>


<p>
If a C or C++ function throws an error, you may want to convert that error into a Python
exception. To do this, you can use the <tt>%exception</tt> directive.  <tt>%exception</tt>
simply lets you rewrite part of the generated wrapper code to include an error check.
</p>

<p>
In C, a function often indicates an error by returning a status code (a negative number
or a NULL pointer perhaps).  Here is a simple example of how you might handle that:
</p>

<div class="code">
<pre>
%exception malloc {
  $action
  if (!result) {
     PyErr_SetString(PyExc_MemoryError,"Not enough memory");
     return NULL;
  }
}
void *malloc(size_t nbytes);
</pre>
</div>

<p>
In Python,
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = example.malloc(2000000000)
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in ?
MemoryError: Not enough memory
&gt;&gt;&gt;
</pre>
</div>

<p>
If a library provides some kind of general error handling framework, you can also use
that.  For example:
</p>

<div class="code">
<pre>
%exception {
   $action
   if (err_occurred()) {
      PyErr_SetString(PyExc_RuntimeError, err_message());
      return NULL;
   }
}
</pre>
</div>

<p>
No declaration name is given to <tt>%exception</tt>, it is applied to all wrapper functions.
</p>

<p>
C++ exceptions are also easy to handle.  For example, you can write code like this:
</p>

<div class="code">
<pre>
%exception getitem {
   try {
      $action
   } catch (std::out_of_range &amp;e) {
      PyErr_SetString(PyExc_IndexError, const_cast&lt;char*&gt;(e.what()));
      return NULL;
   }
}

class Base {
public:
     Foo *getitem(int index);      // Exception handled added
     ...
};
</pre>
</div>

<p>
When raising a Python exception from C, use the <tt>PyErr_SetString()</tt>
function as shown above.  The following exception types can be used as the first argument.
</p>

<div class="code">
<pre>
PyExc_ArithmeticError
PyExc_AssertionError
PyExc_AttributeError
PyExc_EnvironmentError
PyExc_EOFError
PyExc_Exception
PyExc_FloatingPointError
PyExc_ImportError
PyExc_IndexError
PyExc_IOError
PyExc_KeyError
PyExc_KeyboardInterrupt
PyExc_LookupError
PyExc_MemoryError
PyExc_NameError
PyExc_NotImplementedError
PyExc_OSError
PyExc_OverflowError
PyExc_RuntimeError
PyExc_StandardError
PyExc_SyntaxError
PyExc_SystemError
PyExc_TypeError
PyExc_UnicodeError
PyExc_ValueError
PyExc_ZeroDivisionError
</pre>
</div>

<p>
The language-independent <tt>exception.i</tt> library file can also be used
to raise exceptions.  See the <a href="Library.html#Library">SWIG Library</a> chapter.
</p>

<H2><a name="Python_nn45"></a>26.7 Tips and techniques</H2>


<p>
Although SWIG is largely automatic, there are certain types of wrapping problems that
require additional user input.    Examples include dealing with output parameters,
strings, binary data, and arrays.   This chapter discusses the common techniques for
solving these problems.
</p>

<H3><a name="Python_nn46"></a>26.7.1 Input and output parameters</H3>


<p>
A common problem in some C programs is handling parameters passed as simple pointers.  For
example:
</p>

<div class="code">
<pre>
void add(int x, int y, int *result) {
   *result = x + y;
}
</pre>
</div>

<p>
or perhaps
</p>

<div class="code">
<pre>
int sub(int *x, int *y) {
   return *x-*y;
}
</pre>
</div>

<p>
The easiest way to handle these situations is to use the <tt>typemaps.i</tt> file.  For example:
</p>

<div class="code">
<pre>
%module example
%include "typemaps.i"

void add(int, int, int *OUTPUT);
int  sub(int *INPUT, int *INPUT);
</pre>
</div>

<p>
In Python, this allows you to pass simple values.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = add(3,4)
&gt;&gt;&gt; print a
7
&gt;&gt;&gt; b = sub(7,4)
&gt;&gt;&gt; print b
3
&gt;&gt;&gt;
</pre>
</div>

<p>
Notice how the <tt>INPUT</tt> parameters allow integer values to be passed instead of pointers
and how the <tt>OUTPUT</tt> parameter creates a return result.
</p>

<p>
If you don't want to use the names <tt>INPUT</tt> or <tt>OUTPUT</tt>, use the <tt>%apply</tt>
directive.  For example:
</p>

<div class="code">
<pre>
%module example
%include "typemaps.i"

%apply int *OUTPUT { int *result };
%apply int *INPUT  { int *x, int *y};

void add(int x, int y, int *result);
int  sub(int *x, int *y);
</pre>
</div>

<p>
If a function mutates one of its parameters like this,
</p>

<div class="code">
<pre>
void negate(int *x) {
   *x = -(*x);
}
</pre>
</div>

<p>
you can use <tt>INOUT</tt> like this:
</p>

<div class="code">
<pre>
%include "typemaps.i"
...
void negate(int *INOUT);
</pre>
</div>

<p>
In Python, a mutated parameter shows up as a return value.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = negate(3)
&gt;&gt;&gt; print a
-3
&gt;&gt;&gt;
</pre>
</div>

<p>
Note: Since most primitive Python objects are immutable, it is not possible to
perform in-place modification of a Python object passed as a parameter.
</p>

<p>
The most common use of these special typemap rules is to handle functions that
return more than one value.   For example, sometimes a function returns a result
as well as a special error code:
</p>

<div class="code">
<pre>
/* send message, return number of bytes sent, along with success code */
int send_message(char *text, int len, int *success);
</pre>
</div>

<p>
To wrap such a function, simply use the <tt>OUTPUT</tt> rule above. For example:
</p>

<div class="code">
<pre>
%module example
%include "typemaps.i"
%apply int *OUTPUT { int *success };
...
int send_message(char *text, int *success);
</pre>
</div>

<p>
When used in Python, the function will return multiple values.  
</p>

<div class="targetlang">
<pre>
bytes, success = send_message("Hello World")
if not success:
    print "Whoa!"
else:
    print "Sent", bytes
</pre>
</div>

<p>
Another common use of multiple return values are in query functions.  For example:
</p>

<div class="code">
<pre>
void get_dimensions(Matrix *m, int *rows, int *columns);
</pre>
</div>

<p>
To wrap this, you might use the following:
</p>

<div class="code">
<pre>
%module example
%include "typemaps.i"
%apply int *OUTPUT { int *rows, int *columns };
...
void get_dimensions(Matrix *m, int *rows, *columns);
</pre>
</div>

<p>
Now, in Python:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; r,c = get_dimensions(m)
</pre>
</div>

<p>
Be aware that the primary purpose of the <tt>typemaps.i</tt> file is to support primitive datatypes.
Writing a function like this
</p>

<div class="code">
<pre>
void foo(Bar *OUTPUT);
</pre>
</div>

<p>
may not have the intended effect since <tt>typemaps.i</tt> does not define an OUTPUT rule for <tt>Bar</tt>.
</p>

<H3><a name="Python_nn47"></a>26.7.2 Simple pointers</H3>


<p>
If you must work with simple pointers such as <tt>int *</tt> or <tt>double *</tt> and you don't want to use
<tt>typemaps.i</tt>, consider using the <tt>cpointer.i</tt> library file.    For example:
</p>

<div class="code">
<pre>
%module example
%include "cpointer.i"

%inline %{
extern void add(int x, int y, int *result);
%}

%pointer_functions(int, intp);
</pre>
</div>

<p>
The <tt>%pointer_functions(type,name)</tt> macro generates five helper functions that can be used to create,
destroy, copy, assign, and dereference a pointer.  In this case, the functions are as follows:
</p>

<div class="code">
<pre>
int  *new_intp();
int  *copy_intp(int *x);
void  delete_intp(int *x);
void  intp_assign(int *x, int value);
int   intp_value(int *x);
</pre>
</div>

<p>
In Python, you would use the functions like this:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; result = new_intp()
&gt;&gt;&gt; print result
_108fea8_p_int
&gt;&gt;&gt; add(3,4,result)
&gt;&gt;&gt; print intp_value(result)
7
&gt;&gt;&gt;
</pre>
</div>

<p>
If you replace <tt>%pointer_functions()</tt> by <tt>%pointer_class(type,name)</tt>, the interface is more class-like.
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; result = intp()
&gt;&gt;&gt; add(3,4,result)
&gt;&gt;&gt; print result.value()
7
</pre>
</div>

<p>
See the <a href="Library.html#Library">SWIG Library</a> chapter for further details.
</p>

<H3><a name="Python_nn48"></a>26.7.3 Unbounded C Arrays</H3>


<p>
Sometimes a C function expects an array to be passed as a pointer.  For example,
</p>

<div class="code">
<pre>
int sumitems(int *first, int nitems) {
    int i, sum = 0;
    for (i = 0; i &lt; nitems; i++) {
        sum += first[i];
    }
    return sum;
}
</pre>
</div>

<p>
To wrap this into Python, you need to pass an array pointer as the first argument. 
A simple way to do this is to use the <tt>carrays.i</tt> library file.  For example:
</p>

<div class="code">
<pre>
%include "carrays.i"
%array_class(int, intArray);
</pre>
</div>

<p>
The <tt>%array_class(type, name)</tt> macro creates wrappers for an unbounded array object that
can be passed around as a simple pointer like <tt>int *</tt> or <tt>double *</tt>.
For instance, you will be able to do this in Python:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; a = intArray(10000000)         # Array of 10-million integers
&gt;&gt;&gt; for i in xrange(10000):        # Set some values
...     a[i] = i
&gt;&gt;&gt; sumitems(a,10000)
49995000
&gt;&gt;&gt;
</pre>
</div>

<p>
The array "object" created by <tt>%array_class()</tt> does not
encapsulate pointers inside a special array object.  In fact, there is
no bounds checking or safety of any kind (just like in C).  Because of
this, the arrays created by this library are extremely low-level
indeed.  You can't iterate over them nor can you even query their
length.  In fact, any valid memory address can be accessed if you want
(negative indices, indices beyond the end of the array, etc.).
Needless to say, this approach is not going to suit all applications.
On the other hand, this low-level approach is extremely efficient and
well suited for applications in which you need to create buffers,
package binary data, etc.
</p>

<H3><a name="Python_nn49"></a>26.7.4 String handling</H3>


<p>
If a C function has an argument of <tt>char *</tt>, then a Python string
can be passed as input.  For example:
</p>

<div class="code">
<pre>
// C
void foo(char *s);
</pre>
</div>

<div class="targetlang">
<pre>
# Python
&gt;&gt;&gt; foo("Hello")
</pre>
</div>

<p>
When a Python string is passed as a parameter, the C function receives a pointer to the raw
data contained in the string.  Since Python strings are immutable, it is illegal
for your program to change the value.  In fact, doing so will probably crash the Python
interpreter.
</p>

<p>
If your program modifies the input parameter or uses it to return data, consider
using the <tt>cstring.i</tt> library file described in the <a href="Library.html#Library">SWIG Library</a> chapter.
</p>

<p>
When functions return a <tt>char *</tt>, it is assumed to be a NULL-terminated string. 
Data is copied into a new Python string and returned.
</p>

<p>
If your program needs to work with binary data, you can use a typemap
to expand a Python string into a pointer/length argument pair.   As luck would have it, 
just such a typemap is already defined. Just do this:
</p>

<div class="code">
<pre>
%apply (char *STRING, int LENGTH) { (char *data, int size) };
...
int parity(char *data, int size, int initial);
</pre>
</div>

<p>
Now in Python:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; parity("e\x09ffss\x00\x00\x01\nx", 0)
</pre>
</div>

<p>
If you need to return binary data, you might use the
<tt>cstring.i</tt> library file.  The <tt>cdata.i</tt> library can
also be used to extra binary data from arbitrary pointers.
</p>

<H3><a name="Python_nn50"></a>26.7.5 Arrays</H3>


<H3><a name="Python_nn51"></a>26.7.6 String arrays</H3>


<H3><a name="Python_nn52"></a>26.7.7 STL wrappers</H3>


<H2><a name="Python_nn53"></a>26.8 Typemaps</H2>


<p>
This section describes how you can modify SWIG's default wrapping behavior
for various C/C++ datatypes using the <tt>%typemap</tt> directive.   This
is an advanced topic that assumes familiarity with the Python C API as well
as the material in the "<a href="Typemaps.html#Typemaps">Typemaps</a>" chapter.
</p>

<p>
Before proceeding, it should be stressed that typemaps are not a required 
part of using SWIG---the default wrapping behavior is enough in most cases.
Typemaps are only used if you want to change some aspect of the primitive
C-Python interface or if you want to elevate your guru status.
</p>

<H3><a name="Python_nn54"></a>26.8.1 What is a typemap?</H3>


<p>
A typemap is nothing more than a code generation rule that is attached to 
a specific C datatype.   For example, to convert integers from Python to C,
you might define a typemap like this:
</p>

<div class="code"><pre>
%module example

%typemap(in) int {
	$1 = (int) PyLong_AsLong($input);
	printf("Received an integer : %d\n",$1);
}
%inline %{
extern int fact(int n);
%}
</pre></div>

<p>
Typemaps are always associated with some specific aspect of code generation.
In this case, the "in" method refers to the conversion of input arguments
to C/C++.  The datatype <tt>int</tt> is the datatype to which the typemap
will be applied.  The supplied C code is used to convert values.  In this
code a number of special variable prefaced by a <tt>$</tt> are used.  The
<tt>$1</tt> variable is placeholder for a local variable of type <tt>int</tt>.
The <tt>$input</tt> variable is the input object of type <tt>PyObject *</tt>.
</p>

<p>
When this example is compiled into a Python module, it operates as follows:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; from example import *
&gt;&gt;&gt; fact(6)
Received an integer : 6
720
</pre></div>

<p>
In this example, the typemap is applied to all occurrences of the <tt>int</tt> datatype.
You can refine this by supplying an optional parameter name.  For example:
</p>

<div class="code"><pre>
%module example

%typemap(in) int nonnegative {
	$1 = (int) PyLong_AsLong($input);
        if ($1 &lt; 0) {
           PyErr_SetString(PyExc_ValueError,"Expected a nonnegative value.");
           return NULL;
        }
}
%inline %{
extern int fact(int nonnegative);
%}
</pre></div>

<p>
In this case, the typemap code is only attached to arguments that exactly match <tt>int nonnegative</tt>.
</p>

<p>
The application of a typemap to specific datatypes and argument names involves
more than simple text-matching--typemaps are fully integrated into the
SWIG C++ type-system.   When you define a typemap for <tt>int</tt>, that typemap
applies to <tt>int</tt> and qualified variations such as <tt>const int</tt>.  In addition,
the typemap system follows <tt>typedef</tt> declarations.  For example:
</p>

<div class="code">
<pre>
%typemap(in) int n {
	$1 = (int) PyLong_AsLong($input);
	printf("n = %d\n",$1);
}
%inline %{
typedef int Integer;
extern int fact(Integer n);    // Above typemap is applied
%}
</pre>
</div>

<p>
Typemaps can also be defined for groups of consecutive arguments.  For example:
</p>

<div class="code">
<pre>
%typemap(in) (char *str, int len) {
    $1 = PyString_AsString($input);
    $2 = PyString_Size($input);
};

int count(char c, char *str, int len);
</pre>
</div>

<p>
When a multi-argument typemap is defined, the arguments are always handled as a single
Python object.  This allows the function to be used like this (notice how the length
parameter is omitted):
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; example.count('e','Hello World')
1
&gt;&gt;&gt;
</pre>
</div>

<H3><a name="Python_nn55"></a>26.8.2 Python typemaps</H3>


<p>
The previous section illustrated an "in" typemap for converting Python objects to C.
A variety of different typemap methods are defined by the Python module.  For example,
to convert a C integer back into a Python object, you might define an "out" typemap
like this:
</p>

<div class="code">
<pre>
%typemap(out) int {
    $result = PyInt_FromLong((long) $1);
}
</pre>
</div>

<p>
A detailed list of available methods can be found in the "<a
href="Typemaps.html#Typemaps">Typemaps</a>" chapter.  
</p>

<p>
However, the best source of typemap information (and examples) is
probably the Python module itself.  In fact, all of SWIG's default
type handling is defined by typemaps.  You can view these typemaps by
looking at the files in the SWIG library. Just take into account that
in the latest versions of swig (1.3.22+), the library files are not
very pristine clear for the casual reader, as they used to be. The
extensive use of macros and other ugly techniques in the latest
version produce a very powerful and consistent python typemap library,
but at the cost of simplicity and pedagogic value.
</p>

<p>
To learn how to write a simple or your first typemap, you better take
a look at the SWIG library version 1.3.20 or so.
</p>


<H3><a name="Python_nn56"></a>26.8.3 Typemap variables</H3>


<p>
Within typemap code, a number of special variables prefaced with a <tt>$</tt> may appear.
A full list of variables can be found in the "<a href="Typemaps.html#Typemaps">Typemaps</a>" chapter.
This is a list of the most common variables:
</p>

<p>
<tt>$1</tt>
</p>

<div class="indent">
A C local variable corresponding to the actual type specified in the
<tt>%typemap</tt> directive.  For input values, this is a C local variable
that's supposed to hold an argument value.  For output values, this is
the raw result that's supposed to be returned to Python.
</div>

<p>
<tt>$input</tt>
</p>

<div class="indent">
 A <tt>PyObject *</tt> holding a raw Python object with an argument or variable value.
</div>

<p>
<tt>$result</tt>
</p>

<div class="indent">
A <tt>PyObject *</tt> that holds the result to be returned to Python.
</div>

<p>
<tt>$1_name</tt>
</p>

<div class="indent">
The parameter name that was matched. 
</div>

<p>
<tt>$1_type</tt>
</p>

<div class="indent">
The actual C datatype matched by the typemap.
</div>

<p>
<tt>$1_ltype</tt>
</p>

<div class="indent">
An assignable version of the datatype matched by the typemap (a type that can appear on the left-hand-side of
a C assignment operation).  This type is stripped of qualifiers and may be an altered version of <tt>$1_type</tt>.
All arguments and local variables in wrapper functions are declared using this type so that their values can be
properly assigned.
</div>

<p>
<tt>$symname</tt>
</p>

<div class="indent">
The Python name of the wrapper function being created.
</div>

<H3><a name="Python_nn57"></a>26.8.4 Useful Python Functions</H3>


<p>
When you write a typemap, you usually have to work directly with Python objects.
The following functions may prove to be useful.
</p>

<p>
<b>Python Integer Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyInt_FromLong(long l);
long      PyInt_AsLong(PyObject *);
int       PyInt_Check(PyObject *);
</pre>
</div>

<p>
<b>Python Floating Point Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyFloat_FromDouble(double);
double    PyFloat_AsDouble(PyObject *);
int       PyFloat_Check(PyObject *);
</pre>
</div>

<p>
<b>Python String Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyString_FromString(char *);
PyObject *PyString_FromStringAndSize(char *, lint len);
int       PyString_Size(PyObject *);
char     *PyString_AsString(PyObject *);
int       PyString_Check(PyObject *);
</pre>
</div>

<p>
<b>Python List Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyList_New(int size);
int       PyList_Size(PyObject *list);
PyObject *PyList_GetItem(PyObject *list, int i);
int       PyList_SetItem(PyObject *list, int i, PyObject *item);
int       PyList_Insert(PyObject *list, int i, PyObject *item);
int       PyList_Append(PyObject *list, PyObject *item);
PyObject *PyList_GetSlice(PyObject *list, int i, int j);
int       PyList_SetSlice(PyObject *list, int i, int , PyObject *list2);
int       PyList_Sort(PyObject *list);
int       PyList_Reverse(PyObject *list);
PyObject *PyList_AsTuple(PyObject *list);
int       PyList_Check(PyObject *);
</pre>
</div>

<p>
<b>Python Tuple Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyTuple_New(int size);
int       PyTuple_Size(PyObject *);
PyObject *PyTuple_GetItem(PyObject *, int i);
int       PyTuple_SetItem(PyObject *, int i, pyObject *item);
PyObject *PyTuple_GetSlice(PyObject *t, int i, int j);
int       PyTuple_Check(PyObject *);
</pre>
</div>

<p>
<b>Python Dictionary Functions</b>
</p>

<div class="code">
<pre>
write me
</pre>
</div>

<p>
<b>Python File Conversion Functions</b>
</p>

<div class="code">
<pre>
PyObject *PyFile_FromFile(FILE *f);
FILE     *PyFile_AsFile(PyObject *);
int       PyFile_Check(PyObject *);
</pre>
</div>

<p>
<b>Abstract Object Interface</b>
</p>

<div class="code">
<pre>
write me
</pre>
</div>

<H2><a name="Python_nn58"></a>26.9 Typemap Examples</H2>


<p>
This section includes a few examples of typemaps.  For more examples, you
might look at the files "<tt>python.swg</tt>" and "<tt>typemaps.i</tt>" in
the SWIG library.
</p>

<H3><a name="Python_nn59"></a>26.9.1 Converting  Python list to a char ** </H3>


<p>
A common problem in many C programs is the processing of command line
arguments, which are usually passed in an array of NULL terminated
strings.  The following SWIG interface file allows a Python list
object to be used as a <tt>char **</tt> object.
</p>

<div class="code"><pre>
%module argv

// This tells SWIG to treat char ** as a special case
%typemap(in) char ** {
  /* Check if is a list */
  if (PyList_Check($input)) {
    int size = PyList_Size($input);
    int i = 0;
    $1 = (char **) malloc((size+1)*sizeof(char *));
    for (i = 0; i &lt; size; i++) {
      PyObject *o = PyList_GetItem($input,i);
      if (PyString_Check(o))
	$1[i] = PyString_AsString(PyList_GetItem($input,i));
      else {
	PyErr_SetString(PyExc_TypeError,"list must contain strings");
	free($1);
	return NULL;
      }
    }
    $1[i] = 0;
  } else {
    PyErr_SetString(PyExc_TypeError,"not a list");
    return NULL;
  }
}

// This cleans up the char ** array we malloc'd before the function call
%typemap(freearg) char ** {
  free((char *) $1);
}

// Now a test function
%inline %{
int print_args(char **argv) {
    int i = 0;
    while (argv[i]) {
         printf("argv[%d] = %s\n", i,argv[i]);
         i++;
    }
    return i;
}
%}

</pre></div>

<p>
When this module is compiled, the wrapped C function now operates as
follows :
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; from argv import *
&gt;&gt;&gt; print_args(["Dave","Mike","Mary","Jane","John"])
argv[0] = Dave
argv[1] = Mike
argv[2] = Mary
argv[3] = Jane
argv[4] = John
5
</pre></div>

<p>
In the example, two different typemaps are used.  The "in" typemap is
used to receive an input argument and convert it to a C array.  Since dynamic
memory allocation is used to allocate memory for the array, the
"freearg" typemap is used to later release this memory after the execution of
the C function. 
</p>

<H3><a name="Python_nn60"></a>26.9.2 Expanding a Python object into multiple arguments</H3>


<p>
Suppose that you had a collection of C functions with arguments
such as the following:
</p>

<div class="code">
<pre>
int foo(int argc, char **argv);
</pre>
</div>

<p>
In the previous example, a typemap was written to pass a Python list as the <tt>char **argv</tt>.  This
allows the function to be used from Python as follows:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; foo(4, ["foo","bar","spam","1"])
</pre>
</div>

<p>
Although this works, it's a little awkward to specify the argument count.  To fix this, a multi-argument
typemap can be defined.  This is not very difficult--you only have to make slight modifications to the
previous example:
</p>

<div class="code">
<pre>
%typemap(in) (int argc, char **argv) {
  /* Check if is a list */
  if (PyList_Check($input)) {
    int i;
    $1 = PyList_Size($input);
    $2 = (char **) malloc(($1+1)*sizeof(char *));
    for (i = 0; i &lt; $1; i++) {
      PyObject *o = PyList_GetItem($input,i);
      if (PyString_Check(o))
	$2[i] = PyString_AsString(PyList_GetItem($input,i));
      else {
	PyErr_SetString(PyExc_TypeError,"list must contain strings");
	free($2);
	return NULL;
      }
    }
    $2[i] = 0;
  } else {
    PyErr_SetString(PyExc_TypeError,"not a list");
    return NULL;
  }
}

%typemap(freearg) (int argc, char **argv) {
  free((char *) $2);
}
</pre>
</div>

<p>
When writing a multiple-argument typemap, each of the types is referenced by a variable such 
as <tt>$1</tt> or <tt>$2</tt>.   The typemap code simply fills in the appropriate values from
the supplied Python object.
</p>

<p>
With the above typemap in place, you will find it no longer necessary
to supply the argument count.  This is automatically set by the typemap code.  For example:
</p>

<div class="targetlang">
<pre>
&gt;&gt;&gt; foo(["foo","bar","spam","1"])
</pre>
</div>

<H3><a name="Python_nn61"></a>26.9.3 Using typemaps to return arguments</H3>


<p>
A common problem in some C programs is that values may be returned in
arguments rather than in the return value of a function.  For example:
</p>

<div class="code"><pre>
/* Returns a status value and two values in out1 and out2 */
int spam(double a, double b, double *out1, double *out2) {
	... Do a bunch of stuff ...
	*out1 = result1;
	*out2 = result2;
	return status;
};

</pre></div>

<p>
A typemap can be used to handle this case as follows :
</p>

<div class="code"><pre>
%module outarg

// This tells SWIG to treat an double * argument with name 'OutValue' as
// an output value.  We'll append the value to the current result which 
// is guaranteed to be a List object by SWIG.

%typemap(argout) double *OutValue {
    PyObject *o, *o2, *o3;
    o = PyFloat_FromDouble(*$1);
    if ((!$result) || ($result == Py_None)) {
        $result = o;
    } else {
        if (!PyTuple_Check($result)) {
            PyObject *o2 = $result;
            $result = PyTuple_New(1);
            PyTuple_SetItem(target,0,o2);
        }
        o3 = PyTuple_New(1);
        PyTuple_SetItem(o3,0,o);
        o2 = $result;
        $result = PySequence_Concat(o2,o3);
        Py_DECREF(o2);
        Py_DECREF(o3);
    }
}

int spam(double a, double b, double *OutValue, double *OutValue);

</pre></div>

<p>
The typemap works as follows.  First, a check is made to see if any previous result
exists.  If so, it is turned into a tuple and the new output value is concatenated to it.
Otherwise, the result is returned normally.   For the sample function <tt>spam()</tt>, there
are three output values--meaning that the function will return a 3-tuple of the results.
</p>

<p>
As written, the function must accept 4 arguments as input values,
last two being pointers to doubles.  If these arguments are only used to hold output values (and have
no meaningful input value), an additional typemap can be written.  For example:
</p>

<div class="code"><pre>
%typemap(in,numinputs=0) double *OutValue(double temp) {
    $1 = &amp;temp;
}

</pre></div>

<p>
By specifying numinputs=0,  the input value is ignored.  However, since the argument still has to be set to
some meaningful value before calling C, it is set to point to a local variable <tt>temp</tt>.  When the function
stores its output value, it will simply be placed in this local variable.  As a result, the
function can now be used as follows:
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; a = spam(4,5)
&gt;&gt;&gt; print a
(0, 2.45, 5.0)
&gt;&gt;&gt; x,y,z = spam(4,5)
&gt;&gt;&gt;
</pre></div>

<H3><a name="Python_nn62"></a>26.9.4 Mapping Python tuples into small arrays</H3>


<p>
In some applications, it is sometimes desirable to pass small arrays
of numbers as arguments. For example :
</p>

<div class="code"><pre>
extern void set_direction(double a[4]);       // Set direction vector
</pre></div>

<p>
This too, can be handled used typemaps as follows :
</p>

<div class="code"><pre>
// Grab a 4 element array as a Python 4-tuple
%typemap(in) double[4](double temp[4]) {   // temp[4] becomes a local variable
  int i;
  if (PyTuple_Check($input)) {
    if (!PyArg_ParseTuple($input,"dddd",temp,temp+1,temp+2,temp+3)) {
      PyErr_SetString(PyExc_TypeError,"tuple must have 4 elements");
      return NULL;
    }
    $1 = &amp;temp[0];
  } else {
    PyErr_SetString(PyExc_TypeError,"expected a tuple.");
    return NULL;
  }
}

</pre></div>

<p>
This allows our <tt>set_direction</tt> function to be called from
Python as follows :
</p>

<div class="targetlang"><pre>
&gt;&gt;&gt; set_direction((0.5,0.0,1.0,-0.25))
</pre></div>

<p>
Since our mapping copies the contents of a Python tuple into a C
array, such an approach would not be recommended for huge arrays, but
for small structures, this approach works fine.
</p>

<H3><a name="Python_nn63"></a>26.9.5 Mapping sequences to C arrays</H3>


<p>
Suppose that you wanted to generalize the previous example to handle C
arrays of different sizes.  To do this, you might write a typemap as follows:
</p>

<div class="code"><pre>
// Map a Python sequence into any sized C double array
%typemap(in) double[ANY](double temp[$1_dim0]) {
  int i;
  if (!PySequence_Check($input)) {
      PyErr_SetString(PyExc_TypeError,"Expecting a sequence");
      return NULL;
  }
  if (PyObject_Length($input) != $1_dim0) {
      PyErr_SetString(PyExc_ValueError,"Expecting a sequence with $1_dim0 elements");
      return NULL;
  }
  for (i =0; i &lt; $1_dim0; i++) {
      PyObject *o = PySequence_GetItem($input,i);
      if (!PyFloat_Check(o)) {
         PyErr_SetString(PyExc_ValueError,"Expecting a sequence of floats");
         return NULL;
      }
      temp[i] = PyFloat_AsDouble(o);
  }
  $1 = &amp;temp[0];
}
</pre>
</div>

<p>
In this case, the variable <tt>$1_dim0</tt> is expanded to match the
array dimensions actually used in the C code. This allows the typemap
to be applied to types such as:
</p>

<div class="code">
<pre>
void foo(double x[10]);
void bar(double a[4], double b[8]);
</pre>
</div>

<p>
Since the above typemap code gets inserted into every wrapper function where used, it might make sense
to use a helper function instead.  This will greatly reduce the amount of wrapper code.  For example:
</p>

<div class="code">
<pre>
%{
static int convert_darray(PyObject *input, double *ptr, int size) {
  int i;
  if (!PySequence_Check(input)) {
      PyErr_SetString(PyExc_TypeError,"Expecting a sequence");
      return 0;
  }
  if (PyObject_Length(input) != size) {
      PyErr_SetString(PyExc_ValueError,"Sequence size mismatch");
      return 0;
  }
  for (i =0; i &lt; size; i++) {
      PyObject *o = PySequence_GetItem(input,i);
      if (!PyFloat_Check(o)) {
         PyErr_SetString(PyExc_ValueError,"Expecting a sequence of floats");
         return 0;
      }
      ptr[i] = PyFloat_AsDouble(o);
  }
  return 1;
}
%}

%typemap(in) double [ANY](double temp[$1_dim0]) {
   if (!convert_darray($input,temp,$1_dim0))) {
      return NULL;
   }
   $1 = &amp;temp[0];
}
</pre>
</div>

<H3><a name="Python_nn64"></a>26.9.6 Pointer handling</H3>


<p>
Occasionally, it might be necessary to convert pointer values that have
been stored using the SWIG typed-pointer representation.  Since there are
several ways in which pointers can be represented, the following two
functions are used to safely perform this conversion:
</p>

<p>
<tt>
int SWIG_ConvertPtr(PyObject *obj, void **ptr, swig_type_info *ty, int flags)</tt>
</p>

<div class="indent">
Converts a Python object <tt>obj</tt> to a C pointer.  The result of the conversion is placed
into the pointer located at <tt>ptr</tt>.  <tt>ty</tt> is a SWIG type descriptor structure.
<tt>flags</tt> is used to handle error checking and other aspects of conversion.  It is the
bitwise-or of several flag values including <tt>SWIG_POINTER_EXCEPTION</tt> and <tt>SWIG_POINTER_DISOWN</tt>.   The first flag makes the function raise an exception on type error.  The second flag additionally
steals ownership of an object. Returns 0 on success and -1 on error.
</div>

<p>
<tt>
PyObject *Swig_NewPointerObj(void *ptr, swig_type_info *ty, int own)</tt>
</p>

<div class="indent">
Creates a new Python pointer object.  <tt>ptr</tt> is the pointer to convert, <tt>ty</tt> is the SWIG type descriptor structure that
describes the type, and <tt>own</tt> is a flag that indicates whether or not Python should take ownership of the
pointer.
</div>

<p>
Both of these functions require the use of a special SWIG
type-descriptor structure.  This structure contains information about
the mangled name of the datatype, type-equivalence information, as
well as information about converting pointer values under C++
inheritance.   For a type of <tt>Foo *</tt>, the type descriptor structure
is usually accessed as follows:
</p>

<div class="code">
<pre>
Foo *f;
if (SWIG_ConvertPtr($input, (void **) &amp;f, SWIGTYPE_p_Foo, SWIG_POINTER_EXCEPTION) == -1)
  return NULL;

PyObject *obj;
obj = SWIG_NewPointerObj(f, SWIGTYPE_p_Foo, 0);
</pre>
</div>

<p>
In a typemap, the type descriptor should always be accessed using the special typemap
variable <tt>$1_descriptor</tt>.  For example:
</p>

<div class="code">
<pre>
%typemap(in) Foo * {
if ((SWIG_ConvertPtr($input,(void **) &amp;$1, $1_descriptor,SWIG_POINTER_EXCEPTION)) == -1)
  return NULL;
}
</pre>
</div>

<p>
If necessary, the descriptor for any type can be obtained using the <tt>$descriptor()</tt> macro in a typemap.
For example:
</p>

<div class="code">
<pre>
%typemap(in) Foo * {
if ((SWIG_ConvertPtr($input,(void **) &amp;$1, $descriptor(Foo *), 
                                               SWIG_POINTER_EXCEPTION)) == -1)
  return NULL;
}
</pre>
</div>

<p>
Although the pointer handling functions are primarily intended for
manipulating low-level pointers, both functions are fully aware of
Python proxy classes.  Specifically,
<tt>SWIG_ConvertPtr()</tt> will retrieve a pointer from any object
that has a <tt>this</tt> attribute.  In addition,
<tt>SWIG_NewPointerObj()</tt> can automatically generate a proxy
class object (if applicable).
</p>


<H2><a name="Python_nn65"></a>26.10 Docstring Features</H2>


<p>
Usign docstrings in Python code is becoming more and more important
ans more tools are coming on the scene that take advantage of them,
everything from full-blown documentaiton generators to class browsers
and popup call-tips in Python-aware IDEs.  Given the way that SWIG
generates the proxy code by default, your users will normally get
something like <tt>"function_name(*args)"</tt> in the popup calltip of
their IDE which is next to useless when the real function prototype
might be something like this:
</p>

<div class="code">
<pre>
bool function_name(int x, int y, Foo* foo=NULL, Bar* bar=NULL);
</pre>
</div>

<p>
The features described in this section make it easy for you to add
docstrings to your modules, functions and methods that can then be
used by the various tools out there to make the programming experience
of your users much simpler.
</p>


<H3><a name="Python_nn66"></a>26.10.1 Module docstring</H3>


<p>
Python allows a docstring at the begining of the <tt>.py</tt> file
before any other statements, and it is typically used to give a
general description of the entire module.  SWIG supports this by
setting an option of the <tt>%module</tt> directive.  For example:
</p>

<div class="code">
<pre>
%module(docstring="This is the example module's docstring") example
</pre>
</div>

<p>
When you have more than just a line or so then you can retain the easy
readability of the <tt>%module</tt> directive by using a macro.  For
example:
</p>

<div class="code">
<pre>
%define DOCSTRING
"The `XmlResource` class allows program resources defining menus, 
layout of controls on a panel, etc. to be loaded from an XML file."
%enddef

%module(docstring=DOCSTRING) xrc
</pre>
</div>


<H3><a name="Python_nn67"></a>26.10.2 %feature("autodoc")</H3>


<p>
As alluded to above SWIG will generate all the function and method
proxy wrappers with just "*args" (or "*args, **kwargs" if the -keyword
option is used) for a parameter list and will then sort out the
individual parameters in the C wrapper code.  This is nice and simple
for the wrapper code, but makes it difficult to be programmer and tool
friendly as anyone looking at the <tt>.py</tt> file will not be able
to find out anything about the parameters that the fuctions accept.
</p>

<p>But since SWIG does know everything about the fucntion it is
possible to generate a docstring containing the parameter types, names
and default values. Since many of the doctring tools are adopting a
standard of recognizing if the first thing in the docstring is a
function prototype then using that instead of what they found from
introspeciton, then life is good once more.

<p>SWIG's Python module provides support for the "autodoc" feature,
which when attached to a node in the parse tree will cause a docstring
to be generated that includes the name of the funciton, parameter
names, default values if any, and return type if any. There are also
three options for autodoc controlled by the value given to the
feature, described below.

<H4><a name="Python_nn68"></a>26.10.2.1 %feature("autodoc", "0")</H4>


<p>
When the "0" option is given then the types of the parameters will
<em>not</em> be included in the autodoc string.  For example, given
this function prototype:
</p>

<div class="code">
<pre>
%feature("autodoc", "0");
bool function_name(int x, int y, Foo* foo=NULL, Bar* bar=NULL);
</pre>
</div>

<p>
Then Python code like this will be generated:
</p>

<div class="targetlang">
<pre>
def function_name(*args, **kwargs):
    """function_name(x, y, foo=None, bar=None) -&gt; bool"""
    ...
</pre>
</div>


<H4><a name="Python_nn69"></a>26.10.2.2 %feature("autodoc", "1")</H4>


<p>
When the "1" option is used then the parameter types <em>will</em> be
used in the autodoc string.  In addition, an atempt is made to
simplify the type name such that it makes more sense to the Python
user.  Pointer, reference and const info is removed,
<tt>%rename</tt>'s are evaluated, etc.  (This is not always
successful, but works most of the time.  See the next section for what
to do when it doesn't.)  Given the example above, then turning on the
parameter types with the "1" option will result in Python code like
this:
</p>

<div class="targetlang">
<pre>
def function_name(*args, **kwargs):
    """function_name(int x, int y, Foo foo=None, Bar bar=None) -&gt; bool"""
    ...
</pre>
</div>


<H4><a name="Python_nn70"></a>26.10.2.3 %feature("autodoc", "docstring")</H4>


<p>
Finally, there are times when the automatically generated autodoc
string will make no sense for a Python programmer, particularly when a
typemap is involved.  So if you give an explicit value for the autodoc
feature then that string will be used in place of the automatically
generated string.  For example:
</p>

<div class="code">
<pre>
%feature("autodoc", "GetPosition() -&gt; (x, y)") GetPosition;
void GetPosition(int* OUTPUT, int* OUTPUT);
</pre>
</div>


<H3><a name="Python_nn71"></a>26.10.3 %feature("docstring")</H3>


<p>
In addition to the autodoc strings described above, you can also
attach any arbitrary descriptive text to a node in the parse tree with
the "docstring" feature.  When the proxy module is generated then any
docstring associated with classes, function or methods are output.
If an item already has an autodoc string then it is combined with the
docstring and they are output together.  If the docstring is all on a
single line then it is output like this::
</p>

<div class="targetlang">
<pre>
"""This is the docstring"""
</pre>
</div>

<p>
Otherwise, to aid readability it is output like this:
</p>

<div class="targetlang">
<pre>
"""
This is a multi-line docstring
with more than one line.
"""
</pre>
</div>

<H2><a name="Python_nn72"></a>26.11 Python Packages</H2>


<p>
Using the <tt>package</tt> option of the <tt>%module</tt> directive
allows you to specify what Python package that the module will be
living in when installed. 
</p>

<div class="code">
<pre>
%module(package="wx") xrc
</pre>
</div>

<p>
This is useful when the <tt>.i</tt> file is <tt>%import</tt>ed by
another <tt>.i</tt> file.  By default SWIG will assume that the
importer is able to find the importee with just the module name, but
if they live in separate Python packages then that won't work.
However if the importee specifies what its package is with the
<tt>%module</tt> option then the Python code generated for the
importer will use that package name when importing the other module
and also in base class declarations, etc. if the pacakge name is
different than its own.
</p>


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