SWIG and Python

# 19 SWIG and Python

- Overview
- Preliminaries
  - Running SWIG
  - Getting the right header files
  - Compiling a dynamic module
  - Using distutils
  - Static linking
  - Using your module
  - Compilation of C++ extensions
  - Compiling for 64-bit platforms
  - Building Python Extensions under Windows
- A tour of basic C/C++ wrapping
  - Modules
  - Functions
  - Global variables
  - Constants and enums
  - Pointers
  - Structures
  - C++ classes
  - C++ inheritance
  - Pointers, references, values, and arrays
  - C++ overloaded functions
  - C++ operators
  - C++ namespaces
  - C++ templates
  - C++ Smart Pointers
- Further details on the Python class interface
  - Proxy classes
  - Memory management
  - Python 2.2 and classic classes
- Common customization features
  - C/C++ helper functions
  - Adding additional Python code
  - Class extension with %extend
  - Exception handling with %exception
- Tips and techniques
  - Input and output parameters
  - Simple pointers
  - Unbounded C Arrays
  - String handling
  - Arrays
  - String arrays
  - STL wrappers
- Typemaps
  - What is a typemap?
  - Python typemaps
  - Typemap variables
  - Useful Python Functions
- Typemap Examples
  - Converting Python list to a char **
  - Expanding a Python object into multiple arguments
  - Using typemaps to return arguments
  - Mapping Python tuples into small arrays
  - Mapping sequences to C arrays
  - Pointer handling

Caution: This chapter is under repair!

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.

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 "SWIG Basics" chapter.

## 19.1 Overview

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.

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.

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.

## 19.2 Preliminaries

### 19.2.1 Running SWIG

Suppose that you defined a SWIG module such as the following:

>     %module example
>     %{
>     #include "header.h"
>     %}
>     int fact(int n);

To build a Python module, run SWIG using the `-python` option :

>     $ swig -python example.i

If building a C++ extension, add the `-c++` option:

>     $ swig -c++ -python example.i

This creates two different files; a C/C++ source file `example_wrap.c` or `example_wrap.cxx` and a Python source file `example.py`. 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.

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

### 19.2.2 Getting the right header files

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

>     /usr/local/include/python2.0

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:

>     $ 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.
>     >>> import sys
>     >>> print sys.prefix
>     /usr/local
>     >>>

### 19.2.3 Compiling a dynamic module

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 comands like this (shown for Linux):

>     $ swig -python example.i
>     $ gcc -c example.c
>     $ gcc -c example_wrap.c -I/usr/local/include/python2.0
>     $ gcc -shared example.o example_wrap.o -o _example.so

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 `SWIG/Examples/python` 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 SWIG Wiki for additional information.

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

Compatibility Note: 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 `socket` module actually consists of two files; `socket.py` and `_socket.so`. Many other built-in Python modules follow a similar convention.

### 19.2.4 Using distutils

### 19.2.5 Static linking

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 necesssary 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.

The usual procedure for adding a new module to Python involves finding the Python source, adding an entry to the `Modules/Setup` 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.

In earlier versions of SWIG, the `embed.i` library file could be used to rebuild the interpreter. For example:

>     %module example

>     extern int fact(int);
>     extern int mod(int, int);
>     extern double My_variable;

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

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

>     $ 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

You will need to supply the same libraries that were used to build Python the first time. This may include system libraries such as `-lsocket`, `-lnsl`, and `-lpthread`. 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.

Comment: 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).

Compatibility note: The `embed.i` 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).

### 19.2.6 Using your module

To use your module, simply use the Python `import` statement. If all goes well, you will be able to this:

>     $ python
>     >>> import example
>     >>> example.fact(4)
>     24
>     >>>

A common error received by first-time users is the following:

>     >>> import example
>     Traceback (most recent call last):
>       File "", line 1, in ?
>       File "example.py", line 2, in ?
>         import _example
>     ImportError: No module named _example

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 `_example.so`. And don't forget the leading underscore (_).

Another possible error is the following:

>     >>> import example
>     Traceback (most recent call last):
>       File "", line 1, in ?
>     ImportError: dynamic module does not define init function (init_example)
>     >>>

This error is almost always caused when a bad name is given to the shared object file. For example, if you created a file `example.so` instead of `_example.so` 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 `%module` 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.

Another common error is something similar to the following:

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

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.

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 `%ignore` directive to ignore the declaration.

Finally, suppose that your extension module is linked with another library like this:

>     $ gcc -shared example.o example_wrap.o -L/home/beazley/projects/lib -lfoo \
>           -o _example.so

If the `foo` library is compiled as a shared library, you might encounter the following problem when you try to use your module:

>     >>> import example
>     Traceback (most recent call last):
>       File "", line 1, in ?
>     ImportError: libfoo.so: cannot open shared object file: No such file or directory
>     >>>

This error is generated because the dynamic linker can't locate the `libfoo.so` library. When shared libraries are loaded, the system normally only checks a few standard locations such as `/usr/lib` and `/usr/local/lib`. 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:

>     $ gcc -shared example.o example_wrap.o -L/home/beazley/projects/lib -lfoo \
>           -Xlinker -rpath /home/beazley/projects/lib  \
>           -o _example.so

Alternatively, you can set the `LD_LIBRARY_PATH` environment variable to include the directory with your shared libraries. If setting `LD_LIBRARY_PATH`, 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:

>     $ env LD_LIBRARY_PATH=/home/beazley/projects/lib python

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

### 19.2.7 Compilation of C++ extensions

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.

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

>     % 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

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 `-lCrun` like this:

>     % 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

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

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.

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 `ldd` 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:

>     $ ldd swig
>             libstdc++-libc6.1-1.so.2 => /usr/lib/libstdc++-libc6.1-1.so.2 (0x40019000)
>             libm.so.6 => /lib/libm.so.6 (0x4005b000)
>             libc.so.6 => /lib/libc.so.6 (0x40077000)
>             /lib/ld-linux.so.2 => /lib/ld-linux.so.2 (0x40000000)
>     $

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.

### 19.2.8 Compiling for 64-bit platforms

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.

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.

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).

### 19.2.9 Building Python Extensions under Windows

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.

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

- Open up a new workspace and use the AppWizard to select a DLL project.
- 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. `example_wrap.c`). Note : If using C++, choose a different suffix for the wrapper file such as `example_wrap.cxx`. Don't worry if the wrapper file doesn't exist yet--Developer Studio keeps a reference to it.
- Select the SWIG interface file and go to the settings menu. Under settings, select the "Custom Build" option.
- Enter "SWIG" in the description field.
- Enter "`swig -python -o $(ProjDir)\$(InputName)_wrap.c $(InputPath)`" in the "Build command(s) field"
- Enter "`$(ProjDir)\$(InputName)_wrap.c`" in the "Output files(s) field".
- 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".
- Define the symbol __WIN32__ under preprocessor options.
- 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).
- Build your project.

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.

To run your new Python extension, simply run Python and use the `import` command as normal. For example :

>     MSDOS > python
>     >>> import example
>     >>> print example.fact(4)
>     24
>     >>>

If you get an `ImportError` 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.

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 SWIG Wiki.

## 19.3 A tour of basic C/C++ wrapping

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.

### 19.3.1 Modules

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

### 19.3.2 Functions

Global functions are wrapped as new Python built-in functions. For example,

>     %module example
>     int fact(int n);

creates a built-in function `example.fact(n)` that works exactly like you think it does:

>     >>> import example
>     >>> print example.fact(4)
>     24
>     >>>

### 19.3.3 Global variables

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

>     a = 3.4

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

>     b = a

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.

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

>     // SWIG interface file with global variables
>     %module example
>     ...
>     extern int My_variable;
>     extern double density;
>     ...

Now look at the Python interface:

>     >>> import example
>     >>> # Print out value of a C global variable
>     >>> print example.cvar.My_variable
>     4
>     >>> # Set the value of a C global variable
>     >>> example.cvar.density = 0.8442
>     >>> # Use in a math operation
>     >>> example.cvar.density = example.cvar.density*1.10

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

>     >>> example.cvar.density = "Hello"
>     Traceback (most recent call last):
>       File "", line 1, in ?
>     TypeError: C variable 'density (double )'
>     >>>

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

To make ordinary variables read-only, you can use the `%immutable` directive. For example:

>     %immutable;
>     extern char *path;
>     %mutable;

The `%immutable` directive stays in effect until it is explicitly disabled using `%mutable`.

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

>     %immutable path;
>     ...
>     extern char *path;      // Read-only (due to %immutable)

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

>     % swig -python -globals myvar example.i

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

### 19.3.4 Constants and enums

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

>     #define PI 3.14159
>     #define VERSION "1.0"

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

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

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.

Note: declarations declared as `const` are wrapped as read-only variables and will be accessed using the `cvar` object described in the previous section. They are not wrapped as constants. For further discussion about this, see the SWIG Basics chapter.

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.

### 19.3.5 Pointers

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

>     %module example

>     FILE *fopen(const char *filename, const char *mode);
>     int fputs(const char *, FILE *);
>     int fclose(FILE *);

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

>     >>> import example
>     >>> f = example.fopen("junk","w")
>     >>> example.fputs("Hello World\n", f)
>     >>> example.fclose(f)

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--normally an encoded character string like this:

>     >>> print f
>     _c0671108_p_FILE
>     >>>

This pointer value can be freely passed around to different C functions that expect to receive an object of type `FILE *`. 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.

As an alternative to strings, SWIG can encode pointers as a Python CObject type. CObjects are rarely discussed in most Python books or documentation. However, this is a special built-in type that can be used to hold raw C pointer values. Internally, a CObject is just a container that holds a raw `void *` along with some additional information such as a type-string.

If you want to use CObjects instead of strings, compile the SWIG wrapper code with the `-DSWIG_COBJECT_TYPES` option. For example:

>     % swig -python example.i
>     % gcc -c example.c
>     % gcc -c -DSWIG_COBJECT_TYPES example_wrap.c -I/usr/local/include/python2.0
>     % gcc -shared example.o example_wrap.o -o _example.so

The choice of whether or not to use strings or CObjects is mostly a matter of personal preference. There is no significant performance difference between using one type or the other (strings actually appear to be ever-so-slightly faster on the author's machine). Although CObjects feel more natural to some programmers, a disadvantage of this approach is that it makes debugging more difficult. For example, if you are using CObjects, you will get code that works like this:

>     >>> import example
>     >>> f = example.fopen("junk","w")
>     >>> f
>     <PyCObject object at 0x80c5e60>
>     >>>

Notice how no clues regarding the actual type of `f` is shown. On the other hand, the string representation produces the following:

>     >>> f
>     '_c0671108_p_FILE'
>     >>>

For either pointer representation, the NULL pointer is represented by `None`.

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, and it won't work if you switch to a new pointer representation such as CObjects. If you need to cast a pointer or change its value, consider writing some helper functions instead. For example:

>     %inline %{
>     /* C-style cast */
>     Bar *FooToBar(Foo *f) {
>        return (Bar *) f;
>     }

>     /* C++-style cast */
>     Foo *BarToFoo(Bar *b) {
>        return dynamic_cast<Foo*>(b);
>     }

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

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 `None` if the conversion can't be performed.

### 19.3.6 Structures

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

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

is used as follows:

>     >>> v = example.Vector()
>     >>> v.x = 3.5
>     >>> v.y = 7.2
>     >>> print v.x, v.y, v.z
>     7.8 -4.5 0.0
>     >>>

Similar access is provided for unions and the data members of C++ classes.

If you print out the value of `v` in the above example, you will see something like this:

>     >>> print v
>     <C Vector instance at _18e31408_p_Vector>

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 `.this` attribute. For example:

>     >>> print v.this
>     _18e31408_p_Vector
>     >>>

Further details about the Python proxy class are covered a little later.

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

>     struct Foo {
>        ...
>        %immutable;
>        int x;        /* Read-only members */
>        char *name;
>        %mutable;
>        ...
>     };

When `char *` members of a structure are wrapped, the contents are assumed to be dynamically allocated using `malloc` or `new` (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).

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

>     struct Bar {
>         int  x[16];
>     };

If accessed in Python, you will see behavior like this:

>     >>> b = example.Bar()
>     >>> print b.x
>     _801861a4_p_int
>     >>>

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

>     >>> c = example.Bar()
>     >>> c.x = b.x             # Copy contents of b.x to c.x

For array assignment, SWIG copies the entire contents of the array starting with the data pointed to by `b.x`. 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.

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:

>     struct Foo {
>        int a;
>     };

>     struct Bar {
>        Foo f;
>     };

Now, suppose that you access the `f` attribute of `Bar` like this:

>     >>> b = Bar()
>     >>> x = b.f

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

>     Bar b;
>     Foo *x = &b->f;       /* Points inside b */

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

>     >>> b = Bar()
>     >>> b.f.a = 3               # Modify attribute of structure member
>     >>> x = b.f
>     >>> x.a = 3                 # Modifies the same structure

### 19.3.7 C++ classes

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

>     class List {
>     public:
>       List();
>       ~List();
>       int  search(char *item);
>       void insert(char *item);
>       void remove(char *item);
>       char *get(int n);
>       int  length;
>     };

you can use it in Python like this:

>     >>> l = example.List()
>     >>> l.insert("Ale")
>     >>> l.insert("Stout")
>     >>> l.insert("Lager")
>     >>> l.get(1)
>     'Stout'
>     >>> print l.length
>     3
>     >>>

Class data members are accessed in the same manner as C structures.

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:

>     class Spam {
>     public:
>        static void foo();
>        static int bar;

>     };

In Python, the static member can be access in three different ways:

>     >>> example.Spam_foo()    # Spam::foo()
>     >>> s = example.Spam()
>     >>> s.foo()               # Spam::foo() via an instance
>     >>> example.Spam.foo()    # Spam::foo(). Python-2.2 only

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.

Static member variables are currently accessed as global variables. This means, they are accessed through `cvar` like this:

>     >>> print example.cvar.Spam_bar
>     7

### 19.3.8 C++ inheritance

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

>     class Foo {
>     ...
>     };

>     class Bar : public Foo {
>     ...
>     };

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:

>     >>> b = Bar()
>     >>> instance(b,Foo)
>     1
>     >>> issubclass(Bar,Foo)
>     1
>     >>> issubclass(Foo,Bar)
>     0

Furthermore, if you have functions like this

>     void spam(Foo *f);

then the function `spam()` accepts `Foo *` or a pointer to any class derived from `Foo`.

It is safe to use multiple inheritance with SWIG.

### 19.3.9 Pointers, references, values, and arrays

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

>     void spam1(Foo *x);      // Pass by pointer
>     void spam2(Foo &x);      // Pass by reference
>     void spam3(Foo x);       // Pass by value
>     void spam4(Foo x[]);     // Array of objects

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:

>     >>> f = Foo()           # Create a Foo
>     >>> spam1(f)            # Ok. Pointer
>     >>> spam2(f)            # Ok. Reference
>     >>> spam3(f)            # Ok. Value.
>     >>> spam4(f)            # Ok. Array (1 element)

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

>     Foo *spam5();
>     Foo &spam6();
>     Foo  spam7();

then all three functions will return a pointer to some `Foo` object. Since the third function (spam7) 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).

### 19.3.10 C++ overloaded functions

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

>     void foo(int);
>     void foo(char *c);

You can use them in Python in a straightforward manner:

>     >>> foo(3)           # foo(int)
>     >>> foo("Hello")     # foo(char *c)

Similarly, if you have a class like this,

>     class Foo {
>     public:
>         Foo();
>         Foo(const Foo &);
>         ...
>     };

you can write Python code like this:

>     >>> f = Foo()          # Create a Foo
>     >>> g = Foo(f)         # Copy f

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

>     void spam(int);
>     void spam(short);

or

>     void foo(Bar *b);
>     void foo(Bar &b);

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

>     example.i:12: Warning(509): Overloaded spam(short) is shadowed by spam(int) at example.i:11.

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

>     %rename(spam_short) spam(short);
>     ...
>     void spam(int);
>     void spam(short);   // Accessed as spam_short

or

>     %ignore spam(short);
>     ...
>     void spam(int);
>     void spam(short);   // Ignored

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.

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

### 19.3.11 C++ operators

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

>     class Complex {
>     private:
>       double rpart, ipart;
>     public:
>       Complex(double r = 0, double i = 0) : rpart(r), ipart(i) { }
>       Complex(const Complex &c) : rpart(c.rpart), ipart(c.ipart) { }
>       Complex &operator=(const Complex &c);
>       Complex operator+(const Complex &c) const;
>       Complex operator-(const Complex &c) const;
>       Complex operator*(const Complex &c) const;
>       Complex operator-() const;

>       double re() const { return rpart; }
>       double im() const { return ipart; }
>     };

When wrapped, it works like you expect:

>     >>> c = Complex(3,4)
>     >>> d = Complex(7,8)
>     >>> e = c + d
>     >>> e.re()
>     10.0
>     >>> e.im()
>     12.0

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

>     class Complex {
>     ...
>     friend Complex operator+(double, const Complex &c);
>     ...
>     };

then SWIG doesn't know what to do with the friend function--in fact, it simply 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:

>     %rename(Complex_add_dc) operator+(double, const Complex &);
>     ...
>     Complex operator+(double, const Complex &c);

There are ways to make this operator appear as part of the class using the `%extend` directive. Keep reading.

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.

### 19.3.12 C++ namespaces

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,

>     %module example

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

it works in Python as follows:

>     >>> import example
>     >>> example.fact(3)
>     6
>     >>> v = example.Vector()
>     >>> v.x = 3.4
>     >>> print v.y
>     0.0
>     >>>

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

>     %rename(Bar_spam) Bar::spam;

>     namespace Foo {
>         int spam();
>     }

>     namespace Bar {
>         int spam();
>     }

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.

### 19.3.13 C++ templates

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 `%template` directive. For example:

>     %module example
>     %{
>     #include "pair.h"
>     %}

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

>     %template(pairii) pair<int,int>;

In Python:

>     >>> import example
>     >>> p = example.pairii(3,4)
>     >>> p.first
>     3
>     >>> p.second
>     4

Obviously, there is more to template wrapping than shown in this example. More details can be found in the SWIG and C++ chapter. Some more complicated examples will appear later.

### 19.3.14 C++ Smart Pointers

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 `operator->()` like this:

>     template<class T> class SmartPtr {
>        ...
>        T *operator->();
>        ...
>     }

Then, if you have a class like this,

>     class Foo {
>     public:
>          int x;
>          int bar();
>     };

A smart pointer would be used in C++ as follows:

>     SmartPtr<Foo> p = CreateFoo();   // Created somehow (not shown)
>     ...
>     p->x = 3;                        // Foo::x
>     int y = p->bar();                // Foo::bar

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

>     %module example
>     ...
>     %template(SmartPtrFoo) SmartPtr<Foo>;
>     ...

Now, in Python, everything should just "work":

>     >>> p = example.CreateFoo()          # Create a smart-pointer somehow
>     >>> p.x = 3                          # Foo::x
>     >>> p.bar()                          # Foo::bar

If you ever need to access the underlying pointer returned by `operator->()` itself, simply use the `__deref__()` method. For example:

>     >>> f = p.__deref__()     # Returns underlying Foo *

## 19.4 Further details on the Python class interface

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.

### 19.4.1 Proxy classes

In the "SWIG basics" and "SWIG and C++" chapters, details of low-level structure and class wrapping are described. To summarize those chapters, if you have a class like this

>     class Foo {
>     public:
>          int x;
>          int spam(int);
>          ...

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

>     Foo *new_Foo() {
>         return new Foo();
>     }
>     void delete_Foo(Foo *f) {
>         delete f;
>     }
>     int Foo_x_get(Foo *f) {
>         return f->x;
>     }
>     void Foo_x_set(Foo *f, int value) {
>         f->x = value;
>     }
>     int Foo_spam(Foo *f, int arg1) {
>         return f->spam(arg1);
>     }

These wrappers can be found in the low-level extension module (e.g., `_example`).

Using these wrappers, SWIG generates a high-level Python proxy class like this (shown for Python 2.2):

>     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)

This class merely holds a pointer to the underlying C++ object (`.this`) 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:

>     >>> f = example.Foo()
>     >>> f.x = 3
>     >>> y = f.spam(5)

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).

### 19.4.2 Memory management

Associated with proxy object, is an ownership flag `.thisown` 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.

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

>     class Foo {
>     public:
>         Foo();
>         Foo bar();
>     };

In Python:

>     >>> f = Foo()
>     >>> f.thisown
>     1
>     >>> g = f.bar()
>     >>> g.thisown
>     1

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:

>     class Foo {
>     public:
>         ...
>         Foo *spam();
>         ...
>     };

>     >>> f = Foo()
>     >>> s = f.spam()
>     >>> print s.thisown
>     0
>     >>>

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!

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

>     %module example

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

>     Foo *head = 0;

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

>     >>> f = example.Foo()
>     >>> f.thisown
>     1
>     >>> example.cvar.head = f
>     >>> f.thisown
>     0
>     >>>

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:

>     >>> f = example.Foo()
>     >>> g = example.Foo()
>     >>> f.thisown
>     1
>     >>> g.thisown
>     1
>     >>> f.next = g
>     >>> g.thisown
>     0
>     >>>

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:

>     class Node {
>        Object *value;
>     public:
>        void set_value(Object *v) { value = v; }
>        ...
>     };

Now, consider the following Python code:

>     >>> v = Object()           # Create an object
>     >>> n = Node()             # Create a node
>     >>> n.set_value(v)         # Set value
>     >>> v.thisown
>     1
>     >>> del v

In this case, the object `n` is holding a reference to `v` 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 `n` will be holding a stale-pointer. If you're lucky, you will only get a segmentation fault.

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

>     >>> v.thisown = 0

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

### 19.4.3 Python 2.2 and classic classes

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 www.python.org and is not repeated here.

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.

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.

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).

## 19.5 Common customization features

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.

### 19.5.1 C/C++ helper functions

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

>     void set_transform(Image *im, double m[4][4]);

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

>     >>> a = [
>     ...   [1,0,0,0],
>     ...   [0,1,0,0],
>     ...   [0,0,1,0],
>     ...   [0,0,0,1]]
>     >>> set_transform(im,a)
>     Traceback (most recent call last):
>       File "", line 1, in ?
>     TypeError: Type error. Expected _p_a_4__double

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

>     %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];
>     }
>     %}

From Python, you could then write code like this:

>     >>> a = new_mat44()
>     >>> mat44_set(a,0,0,1.0)
>     >>> mat44_set(a,1,1,1.0)
>     >>> mat44_set(a,2,2,1.0)
>     ...
>     >>> set_transform(im,a)
>     >>>

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.

### 19.5.2 Adding additional Python code

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 `.py` file created by SWIG. One use of Python code might be to supply a high-level interface to certain functions. For example:

>     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)
>     %}

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

>     >>> a = [
>     ...   [1,0,0,0],
>     ...   [0,1,0,0],
>     ...   [0,0,1,0],
>     ...   [0,0,0,1]]
>     >>> set_transform(im,a)
>     >>>

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.

### 19.5.3 Class extension with %extend

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:

>     %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->x,self->y,self->z);
>            return tmp;
>        }
>        Vector(double x, double y, double z) {
>            Vector *v = (Vector *) malloc(sizeof(Vector));
>            v->x = x;
>            v->y = y;
>            v->z = z;
>            return v;
>        }
>     };

Now, in Python

>     >>> v = example.Vector(2,3,4)
>     >>> print v
>     Vector(2,3,4)
>     >>>

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

>     %extend Vector {
>         Vector __add__(Vector *other) {
>              Vector v;
>              v.x = self->x + other->x;
>              v.y = self->y + other->y;
>              v.z = self->z + other->z;
>              return v;
>         }
>     };

Use it like this:

>     >>> import example
>     >>> v = example.Vector(2,3,4)
>     >>> w = example.Vector(10,11,12)
>     >>> print v+w
>     Vector(12,14,16)
>     >>>

`%extend` 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.

### 19.5.4 Exception handling with %exception

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 `%exception` directive. `%exception` simply lets you rewrite part of the generated wrapper code to include an error check.

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:

>     %exception malloc {
>       $action
>       if (!result) {
>          PyErr_SetString(PyExc_MemoryError,"Not enough memory");
>          return NULL;
>       }
>     }
>     void *malloc(size_t nbytes);

In Python,

>     >>> a = example.malloc(2000000000)
>     Traceback (most recent call last):
>       File "", line 1, in ?
>     MemoryError: Not enough memory
>     >>>

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

>     %exception {
>        $action
>        if (err_occurred()) {
>           PyErr_SetString(PyExc_RuntimeError, err_message());
>           return NULL;
>        }
>     }

No declaration name is given to `%exception`, it is applied to all wrapper functions.

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

>     %exception getitem {
>        try {
>           $action
>        } catch (std::out_of_range &e) {
>           PyErr_SetString(PyExc_IndexError, const_cast(e.what()));
>           return NULL;
>        }
>     }

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

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

>     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

The language-independent `exception.i` library file can also be used to raise exceptions. See the SWIG Library chapter.

## 19.6 Tips and techniques

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.

### 19.6.1 Input and output parameters

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

>     void add(int x, int y, int *result) {
>        *result = x + y;
>     }

or perhaps

>     int sub(int *x, int *y) {
>        return *x-*y;
>     }

The easiest way to handle these situations is to use the `typemaps.i` file. For example:

>     %module example
>     %include "typemaps.i"

>     void add(int, int, int *OUTPUT);
>     int  sub(int *INPUT, int *INPUT);

In Python, this allows you to pass simple values. For example:

>     >>> a = add(3,4)
>     >>> print a
>     7
>     >>> b = sub(7,4)
>     >>> print b
>     3
>     >>>

Notice how the `INPUT` parameters allow integer values to be passed instead of pointers and how the `OUTPUT` parameter creates a return result.

If you don't want to use the names `INPUT` or `OUTPUT`, use the `%apply` directive. For example:

>     %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);

If a function mutates one of its parameters like this,

>     void negate(int *x) {
>        *x = -(*x);
>     }

you can use `INOUT` like this:

>     %include "typemaps.i"
>     ...
>     void negate(int *INOUT);

In Python, a mutated parameter shows up as a return value. For example:

>     >>> a = negate(3)
>     >>> print a
>     -3
>     >>>

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.

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:

>     /* send message, return number of bytes sent, along with success code */
>     int send_message(char *text, int len, int *success);

To wrap such a function, simply use the `OUTPUT` rule above. For example:

>     %module example
>     %include "typemaps.i"
>     %apply int *OUTPUT { int *success };
>     ...
>     int send_message(char *text, int *success);

When used in Python, the function will return multiple values.

>     bytes, success = send_message("Hello World")
>     if not success:
>         print "Whoa!"
>     else:
>         print "Sent", bytes

Another common use of multiple return values are in query functions. For example:

>     void get_dimensions(Matrix *m, int *rows, int *columns);

To wrap this, you might use the following:

>     %module example
>     %include "typemaps.i"
>     %apply int *OUTPUT { int *rows, int *columns };
>     ...
>     void get_dimensions(Matrix *m, int *rows, *columns);

Now, in Python:

>     >>> r,c = get_dimensions(m)

Be aware that the primary purpose of the `typemaps.i` file is to support primitive datatypes. Writing a function like this

>     void foo(Bar *OUTPUT);

may not have the intended effect since `typemaps.i` does not define an OUTPUT rule for `Bar`.

### 19.6.2 Simple pointers

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

>     %module example
>     %include "cpointer.i"

>     extern void add(int x, int y, int *result);
>     %pointer_functions(int, intp);

The `%pointer_functions(type,name)` 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:

>     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);

In Python, you would use the functions like this:

>     >>> result = new_intp()
>     >>> print result
>     _108fea8_p_int
>     >>> add(3,4,result)
>     >>> print intp_value(result)
>     7
>     >>>

If you replace `%pointer_functions()` by `%pointer_class(type,name)`, the interface is more class-like.

>     >>> result = intp()
>     >>> add(3,4,result)
>     >>> print result.value()
>     7

See the SWIG Library chapter for further details.

### 19.6.3 Unbounded C Arrays

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

>     int sumitems(int *first, int nitems) {
>         int i, sum = 0;
>         for (i = 0; i < nitems; i++) {
>             sum += first[i];
>         }
>         return sum;
>     }

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 `carrays.i` library file. For example:

>     %include "carrays.i"
>     %array_class(int, intArray);

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

>     >>> a = intArray(10000000)         # Array of 10-million integers
>     >>> for i in xrange(10000):        # Set some values
>     ...     a[i] = i
>     >>> sumitems(a,10000)
>     49995000
>     >>>

The array "object" created by `%array_class()` 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.

### 19.6.4 String handling

If a C function has an argument of `char *`, then a Python string can be passed as input. For example:

>     // C
>     void foo(char *s);

>     # Python
>     >>> foo("Hello")

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.

If your program modifies the input parameter or uses it to return data, consider using the `cstring.i` library file described in the SWIG Library chapter.

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

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:

>     %apply (char *STRING, int LENGTH) { (char *data, int size) };
>     ...
>     int parity(char *data, int size, int initial);

Now in Python:

>     >>> parity("e\x09ffss\x00\x00\x01\nx", 0)

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

### 19.6.5 Arrays

### 19.6.6 String arrays

### 19.6.7 STL wrappers

## 19.7 Typemaps

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

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.

### 19.7.1 What is a typemap?

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:

>     %module example

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

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 `int` 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 `$` are used. The `$1` variable is placeholder for a local variable of type `int`. The `$input` variable is the input object of type `PyObject *`.

When this example is compiled into a Python module, it operates as follows:

>     >>> from example import *
>     >>> fact(6)
>     Received an integer : 6
>     720

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

>     %module example

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

In this case, the typemap code is only attached to arguments that exactly match `int nonnegative`.

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 `int`, that typemap applies to `int` and qualified variations such as `const int`. In addition, the typemap system follows `typedef` declarations. For example:

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

Typemaps can also be defined for groups of consecutive arguments. For example:

>     %typemap(in) (char *str, int len) {
>         $1 = PyString_AsString($input);
>         $2 = PyString_Size($input);
>     };

>     int count(char c, char *str, int len);

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):

>     >>> example.count('e','Hello World')
>     1
>     >>>

### 19.7.2 Python typemaps

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:

>     %typemap(out) int {
>         $result = PyInt_FromLong((long) $1);
>     }

A detailed list of available methods can be found in the "Typemaps" chapter. 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 `python.swg` file in the SWIG library. Just issue these commands:

>     $ swig -python -co python.swg
>     'python.swg' checked out from the SWIG library.
>     $ cat python.swg

Additional typemap examples can also be found in the `typemaps.i` file.

### 19.7.3 Typemap variables

Within typemap code, a number of special variables prefaced with a `$` may appear. A full list of variables can be found in the "Typemaps" chapter. This is a list of the most common variables:

`$1`

> A C local variable corresponding to the actual type specified in the `%typemap` 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.

`$input`

> A `PyObject *` holding a raw Python object with an argument or variable value.

`$result`

> A `PyObject *` that holds the result to be returned to Python.

`$1_name`

> The parameter name that was matched.

`$1_type`

> The actual C datatype matched by the typemap.

`$1_ltype`

> 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 `$1_type`. All arguments and local variables in wrapper functions are declared using this type so that their values can be properly assigned.

`$symname`

> The Python name of the wrapper function being created.

### 19.7.4 Useful Python Functions

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

Python Integer Functions

>     PyObject *PyInt_FromLong(long l);
>     long      PyInt_AsLong(PyObject *);
>     int       PyInt_Check(PyObject *);

Python Floating Point Functions

>     PyObject *PyFloat_FromDouble(double);
>     double    PyFloat_AsDouble(PyObject *);
>     int       PyFloat_Check(PyObject *);

Python String Functions

>     PyObject *PyString_FromString(char *);
>     PyObject *PyString_FromStringAndSize(char *, lint len);
>     int       PyString_Size(PyObject *);
>     char     *PyString_AsString(PyObject *);
>     int       PyString_Check(PyObject *);

Python List Functions

>     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 *);

Python Tuple Functions

>     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 *);

Python Dictionary Functions

>     write me

Python File Conversion Functions

>     PyObject *PyFile_FromFile(FILE *f);
>     FILE     *PyFile_AsFile(PyObject *);
>     int       PyFile_Check(PyObject *);

Abstract Object Interface

>     write me

## 19.8 Typemap Examples

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

### 19.8.1 Converting Python list to a char **

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 `char **` object.

>     %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;
>         $1 = (char **) malloc((size+1)*sizeof(char *));
>         for (i = 0; i < size; i++) {
>           PyObject *o = PyList_GetItem($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;
>     }
>     %}

When this module is compiled, the wrapped C function now operates as follows :

>     >>> from argv import *
>     >>> print_args(["Dave","Mike","Mary","Jane","John"])
>     argv[0] = Dave
>     argv[1] = Mike
>     argv[2] = Mary
>     argv[3] = Jane
>     argv[4] = John
>     5

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.

### 19.8.2 Expanding a Python object into multiple arguments

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

>     int foo(int argc, char **argv);

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

>     >>> foo(4, ["foo","bar","spam","1"])

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:

>     %typemap(in) (int argc, char **argv) {
>       /* Check if is a list */
>       if (PyList_Check($input)) {
>         int i;
>         $1 = PyList_Size($input);
>         $2 = (char **) malloc((size+1)*sizeof(char *));
>         for (i = 0; i < $1; i++) {
>           PyObject *o = PyList_GetItem($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);
>     }

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

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:

>     >>> foo(["foo","bar","spam","1"])

### 19.8.3 Using typemaps to return arguments

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 :

>     /* 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;
>     };

A typemap can be used to handle this case as follows :

>     %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);

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 `spam()`, there are three output values--meaning that the function will return a 3-tuple of the results.

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:

>     %typemap(in,numinputs=0) double *OutValue(double temp) {
>         $1 = &temp;
>     }

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 `temp`. 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:

>     >>> a = spam(4,5)
>     >>> print a
>     (0, 2.45, 5.0)
>     >>> x,y,z = spam(4,5)
>     >>>

### 19.8.4 Mapping Python tuples into small arrays

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

>     extern void set_direction(double a[4]);       // Set direction vector

This too, can be handled used typemaps as follows :

>     // 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 = &temp[0];
>       } else {
>         PyErr_SetString(PyExc_TypeError,"expected a tuple.");
>         return NULL;
>       }
>     }

This allows our `set_direction` function to be called from Python as follows :

>     >>> set_direction((0.5,0.0,1.0,-0.25))

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.

### 19.8.5 Mapping sequences to C arrays

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:

>     // 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 < $1_dim0; i++) {
>           PyObject *o = PySequence_GetItem($input,i);
>           if (!PyFloat_Check(o)) {
>              PyErr_SetString(PyExc_ValueError,"Expecting a sequence of floats");
>              return NULL;
>           }
>           temp[i] = PyFloat_AsDouble(o);
>       }
>       $1 = &temp[0];
>     }

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

>     void foo(double x[10]);
>     void bar(double a[4], double b[8]);

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:

>     %{
>     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 < 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 = &temp[0];
>     }

### 19.8.6 Pointer handling

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:

`int SWIG_ConvertPtr(PyObject *obj, void **ptr, swig_type_info *ty, int flags)`

> Converts a Python object `obj` to a C pointer. The result of the conversion is placed into the pointer located at `ptr`. `ty` is a SWIG type descriptor structure. `flags` is used to handle error checking and other aspects of conversion. It is the bitwise-or of several flag values including `SWIG_POINTER_EXCEPTION` and `SWIG_POINTER_DISOWN`. 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.

`PyObject *Swig_NewPointerObj(void *ptr, swig_type_info *ty, int own)`

> Creates a new Python pointer object. `ptr` is the pointer to convert, `ty` is the SWIG type descriptor structure that describes the type, and `own` is a flag that indicates whether or not Python should take ownership of the pointer.

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 `Foo *`, the type descriptor structure is usually accessed as follows:

>     Foo *f;
>     if (SWIG_ConvertPtr($input, (void **) &f, SWIGTYPE_p_Foo, SWIG_POINTER_EXCEPTION) == -1) return NULL;

>     PyObject *obj;
>     obj = SWIG_NewPointerObj(f, SWIGTYPE_p_Foo, 0);

In a typemap, the type descriptor should always be accessed using the special typemap variable `$1_descriptor`. For example:

>     %typemap(in) Foo * {
>        if ((SWIG_ConvertPtr($input,(void **) &$1, $1_descriptor,SWIG_POINTER_EXCEPTION)) == -1) return NULL;
>     }

If necessary, the descriptor for any type can be obtained using the `$descriptor()` macro in a typemap. For example:

>     %typemap(in) Foo * {
>        if ((SWIG_ConvertPtr($input,(void **) &$1, $descriptor(Foo *), SWIG_POINTER_EXCEPTION)) == -1) return NULL;
>     }

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