\chapter{Extending Python with \C{} or \Cpp{} \label{intro}}

It is quite easy to add new built-in modules to Python, if you know

how to program in C. Such \dfn{extension modules} can do two things

that can't be done directly in Python: they can implement new built-in

object types, and they can call C library functions and system calls.

To support extensions, the Python API (Application Programmers

Interface) defines a set of functions, macros and variables that

provide access to most aspects of the Python run-time system. The

Python API is incorporated in a C source file by including the header

\code{"Python.h"}.

The compilation of an extension module depends on its intended use as

well as on your system setup; details are given in later chapters.

\section{A Simple Example

\label{simpleExample}}

Let's create an extension module called \samp{spam} (the favorite food

of Monty Python fans...) and let's say we want to create a Python

interface to the C library function \cfunction{system()}.\footnote{An

interface for this function already exists in the standard module

\module{os} --- it was chosen as a simple and straightforward example.}

This function takes a null-terminated character string as argument and

returns an integer. We want this function to be callable from Python

as follows:

\begin{verbatim}

>>> import spam

>>> status = spam.system("ls -l")

\end{verbatim}

Begin by creating a file \file{spammodule.c}. (Historically, if a

module is called \samp{spam}, the C file containing its implementation

is called \file{spammodule.c}; if the module name is very long, like

\samp{spammify}, the module name can be just \file{spammify.c}.)

The first line of our file can be:

\begin{verbatim}

#include <Python.h>

\end{verbatim}

which pulls in the Python API (you can add a comment describing the

purpose of the module and a copyright notice if you like).

\begin{notice}[warning]

Since Python may define some pre-processor definitions which affect

the standard headers on some systems, you \emph{must} include

\file{Python.h} before any standard headers are included.

\end{notice}

All user-visible symbols defined by \file{Python.h} have a prefix of

\samp{Py} or \samp{PY}, except those defined in standard header files.

For convenience, and since they are used extensively by the Python

interpreter, \code{"Python.h"} includes a few standard header files:

\code{<stdio.h>}, \code{<string.h>}, \code{<errno.h>}, and

\code{<stdlib.h>}. If the latter header file does not exist on your

system, it declares the functions \cfunction{malloc()},

\cfunction{free()} and \cfunction{realloc()} directly.

The next thing we add to our module file is the C function that will

be called when the Python expression \samp{spam.system(\var{string})}

is evaluated (we'll see shortly how it ends up being called):

\begin{verbatim}

static PyObject *

spam_system(PyObject *self, PyObject *args)

{

const char *command;

int sts;

if (!PyArg_ParseTuple(args, "s", &command))

return NULL;

sts = system(command);

return Py_BuildValue("i", sts);

}

\end{verbatim}

There is a straightforward translation from the argument list in

Python (for example, the single expression \code{"ls -l"}) to the

arguments passed to the C function. The C function always has two

arguments, conventionally named \var{self} and \var{args}.

The \var{self} argument is only used when the C function implements a

built-in method, not a function. In the example, \var{self} will

always be a \NULL{} pointer, since we are defining a function, not a

method. (This is done so that the interpreter doesn't have to

understand two different types of C functions.)

The \var{args} argument will be a pointer to a Python tuple object

containing the arguments. Each item of the tuple corresponds to an

argument in the call's argument list. The arguments are Python

objects --- in order to do anything with them in our C function we have

to convert them to C values. The function \cfunction{PyArg_ParseTuple()}

in the Python API checks the argument types and converts them to C

values. It uses a template string to determine the required types of

the arguments as well as the types of the C variables into which to

store the converted values. More about this later.

\cfunction{PyArg_ParseTuple()} returns true (nonzero) if all arguments have

the right type and its components have been stored in the variables

whose addresses are passed. It returns false (zero) if an invalid

argument list was passed. In the latter case it also raises an

appropriate exception so the calling function can return

\NULL{} immediately (as we saw in the example).

\section{Intermezzo: Errors and Exceptions

\label{errors}}

An important convention throughout the Python interpreter is the

following: when a function fails, it should set an exception condition

and return an error value (usually a \NULL{} pointer). Exceptions

are stored in a static global variable inside the interpreter; if this

variable is \NULL{} no exception has occurred. A second global

variable stores the ``associated value'' of the exception (the second

argument to \keyword{raise}). A third variable contains the stack

traceback in case the error originated in Python code. These three

variables are the C equivalents of the Python variables

\code{sys.exc_type}, \code{sys.exc_value} and \code{sys.exc_traceback} (see

the section on module \module{sys} in the

\citetitle[../lib/lib.html]{Python Library Reference}). It is

important to know about them to understand how errors are passed

around.

The Python API defines a number of functions to set various types of

exceptions.

The most common one is \cfunction{PyErr_SetString()}. Its arguments

are an exception object and a C string. The exception object is

usually a predefined object like \cdata{PyExc_ZeroDivisionError}. The

C string indicates the cause of the error and is converted to a

Python string object and stored as the ``associated value'' of the

exception.

Another useful function is \cfunction{PyErr_SetFromErrno()}, which only

takes an exception argument and constructs the associated value by

inspection of the global variable \cdata{errno}. The most

general function is \cfunction{PyErr_SetObject()}, which takes two object

arguments, the exception and its associated value. You don't need to

\cfunction{Py_INCREF()} the objects passed to any of these functions.

You can test non-destructively whether an exception has been set with

\cfunction{PyErr_Occurred()}. This returns the current exception object,

or \NULL{} if no exception has occurred. You normally don't need

to call \cfunction{PyErr_Occurred()} to see whether an error occurred in a

function call, since you should be able to tell from the return value.

When a function \var{f} that calls another function \var{g} detects

that the latter fails, \var{f} should itself return an error value

(usually \NULL{} or \code{-1}). It should \emph{not} call one of the

\cfunction{PyErr_*()} functions --- one has already been called by \var{g}.

\var{f}'s caller is then supposed to also return an error indication

to \emph{its} caller, again \emph{without} calling \cfunction{PyErr_*()},

and so on --- the most detailed cause of the error was already

reported by the function that first detected it. Once the error

reaches the Python interpreter's main loop, this aborts the currently

executing Python code and tries to find an exception handler specified

by the Python programmer.

(There are situations where a module can actually give a more detailed

error message by calling another \cfunction{PyErr_*()} function, and in

such cases it is fine to do so. As a general rule, however, this is

not necessary, and can cause information about the cause of the error

to be lost: most operations can fail for a variety of reasons.)

To ignore an exception set by a function call that failed, the exception

condition must be cleared explicitly by calling \cfunction{PyErr_Clear()}.

The only time C code should call \cfunction{PyErr_Clear()} is if it doesn't

want to pass the error on to the interpreter but wants to handle it

completely by itself (possibly by trying something else, or pretending

nothing went wrong).

Every failing \cfunction{malloc()} call must be turned into an

exception --- the direct caller of \cfunction{malloc()} (or

\cfunction{realloc()}) must call \cfunction{PyErr_NoMemory()} and

return a failure indicator itself. All the object-creating functions

(for example, \cfunction{PyInt_FromLong()}) already do this, so this

note is only relevant to those who call \cfunction{malloc()} directly.

Also note that, with the important exception of

\cfunction{PyArg_ParseTuple()} and friends, functions that return an

integer status usually return a positive value or zero for success and

\code{-1} for failure, like \UNIX{} system calls.

Finally, be careful to clean up garbage (by making

\cfunction{Py_XDECREF()} or \cfunction{Py_DECREF()} calls for objects

you have already created) when you return an error indicator!

The choice of which exception to raise is entirely yours. There are

predeclared C objects corresponding to all built-in Python exceptions,

such as \cdata{PyExc_ZeroDivisionError}, which you can use directly.

Of course, you should choose exceptions wisely --- don't use

\cdata{PyExc_TypeError} to mean that a file couldn't be opened (that

should probably be \cdata{PyExc_IOError}). If something's wrong with

the argument list, the \cfunction{PyArg_ParseTuple()} function usually

raises \cdata{PyExc_TypeError}. If you have an argument whose value

must be in a particular range or must satisfy other conditions,

\cdata{PyExc_ValueError} is appropriate.

You can also define a new exception that is unique to your module.

For this, you usually declare a static object variable at the

beginning of your file:

\begin{verbatim}

static PyObject *SpamError;

\end{verbatim}

and initialize it in your module's initialization function

(\cfunction{initspam()}) with an exception object (leaving out

the error checking for now):

\begin{verbatim}

PyMODINIT_FUNC

initspam(void)

{

PyObject *m;

m = Py_InitModule("spam", SpamMethods);

if (m == NULL)

return;

SpamError = PyErr_NewException("spam.error", NULL, NULL);

Py_INCREF(SpamError);

PyModule_AddObject(m, "error", SpamError);

}

\end{verbatim}

Note that the Python name for the exception object is

\exception{spam.error}. The \cfunction{PyErr_NewException()} function

may create a class with the base class being \exception{Exception}

(unless another class is passed in instead of \NULL), described in the

\citetitle[../lib/lib.html]{Python Library Reference} under ``Built-in

Exceptions.''

Note also that the \cdata{SpamError} variable retains a reference to

the newly created exception class; this is intentional! Since the

exception could be removed from the module by external code, an owned

reference to the class is needed to ensure that it will not be

discarded, causing \cdata{SpamError} to become a dangling pointer.

Should it become a dangling pointer, C code which raises the exception

could cause a core dump or other unintended side effects.

We discuss the use of PyMODINIT_FUNC as a function return type later in this

sample.

\section{Back to the Example

\label{backToExample}}

Going back to our example function, you should now be able to

understand this statement:

\begin{verbatim}

if (!PyArg_ParseTuple(args, "s", &command))

return NULL;

\end{verbatim}

It returns \NULL{} (the error indicator for functions returning

object pointers) if an error is detected in the argument list, relying

on the exception set by \cfunction{PyArg_ParseTuple()}. Otherwise the

string value of the argument has been copied to the local variable

\cdata{command}. This is a pointer assignment and you are not supposed

to modify the string to which it points (so in Standard C, the variable

\cdata{command} should properly be declared as \samp{const char

*command}).

The next statement is a call to the \UNIX{} function

\cfunction{system()}, passing it the string we just got from

\cfunction{PyArg_ParseTuple()}:

\begin{verbatim}

sts = system(command);

\end{verbatim}

Our \function{spam.system()} function must return the value of

\cdata{sts} as a Python object. This is done using the function

\cfunction{Py_BuildValue()}, which is something like the inverse of

\cfunction{PyArg_ParseTuple()}: it takes a format string and an

arbitrary number of C values, and returns a new Python object.

More info on \cfunction{Py_BuildValue()} is given later.

\begin{verbatim}

return Py_BuildValue("i", sts);

\end{verbatim}

In this case, it will return an integer object. (Yes, even integers

are objects on the heap in Python!)

If you have a C function that returns no useful argument (a function

returning \ctype{void}), the corresponding Python function must return

\code{None}. You need this idiom to do so (which is implemented by the

\csimplemacro{Py_RETURN_NONE} macro):

\begin{verbatim}

Py_INCREF(Py_None);

return Py_None;

\end{verbatim}

\cdata{Py_None} is the C name for the special Python object

\code{None}. It is a genuine Python object rather than a \NULL{}

pointer, which means ``error'' in most contexts, as we have seen.

\section{The Module's Method Table and Initialization Function

\label{methodTable}}

I promised to show how \cfunction{spam_system()} is called from Python

programs. First, we need to list its name and address in a ``method

table'':

\begin{verbatim}

static PyMethodDef SpamMethods[] = {

...

{"system", spam_system, METH_VARARGS,

"Execute a shell command."},

...

{NULL, NULL, 0, NULL} /* Sentinel */

};

\end{verbatim}

Note the third entry (\samp{METH_VARARGS}). This is a flag telling

the interpreter the calling convention to be used for the C

function. It should normally always be \samp{METH_VARARGS} or

\samp{METH_VARARGS | METH_KEYWORDS}; a value of \code{0} means that an

obsolete variant of \cfunction{PyArg_ParseTuple()} is used.

When using only \samp{METH_VARARGS}, the function should expect

the Python-level parameters to be passed in as a tuple acceptable for

parsing via \cfunction{PyArg_ParseTuple()}; more information on this

function is provided below.

The \constant{METH_KEYWORDS} bit may be set in the third field if

keyword arguments should be passed to the function. In this case, the

C function should accept a third \samp{PyObject *} parameter which

will be a dictionary of keywords. Use

\cfunction{PyArg_ParseTupleAndKeywords()} to parse the arguments to

such a function.

The method table must be passed to the interpreter in the module's

initialization function. The initialization function must be named

\cfunction{init\var{name}()}, where \var{name} is the name of the

module, and should be the only non-\keyword{static} item defined in

the module file:

\begin{verbatim}

PyMODINIT_FUNC

initspam(void)

{

(void) Py_InitModule("spam", SpamMethods);

}

\end{verbatim}

Note that PyMODINIT_FUNC declares the function as \code{void} return type,

declares any special linkage declarations required by the platform, and for

\Cpp{} declares the function as \code{extern "C"}.

When the Python program imports module \module{spam} for the first

time, \cfunction{initspam()} is called. (See below for comments about

embedding Python.) It calls

\cfunction{Py_InitModule()}, which creates a ``module object'' (which

is inserted in the dictionary \code{sys.modules} under the key

\code{"spam"}), and inserts built-in function objects into the newly

created module based upon the table (an array of \ctype{PyMethodDef}

structures) that was passed as its second argument.

\cfunction{Py_InitModule()} returns a pointer to the module object

that it creates (which is unused here). It may abort with a fatal error

for certain errors, or return \NULL{} if the module could not be

initialized satisfactorily.

When embedding Python, the \cfunction{initspam()} function is not

called automatically unless there's an entry in the

\cdata{_PyImport_Inittab} table. The easiest way to handle this is to

statically initialize your statically-linked modules by directly

calling \cfunction{initspam()} after the call to

\cfunction{Py_Initialize()}:

\begin{verbatim}

int

main(int argc, char *argv[])

{

/* Pass argv[0] to the Python interpreter */

Py_SetProgramName(argv[0]);

/* Initialize the Python interpreter. Required. */

Py_Initialize();

/* Add a static module */

initspam();

\end{verbatim}

An example may be found in the file \file{Demo/embed/demo.c} in the

Python source distribution.

\note{Removing entries from \code{sys.modules} or importing

compiled modules into multiple interpreters within a process (or

following a \cfunction{fork()} without an intervening

\cfunction{exec()}) can create problems for some extension modules.

Extension module authors should exercise caution when initializing

internal data structures.

Note also that the \function{reload()} function can be used with

extension modules, and will call the module initialization function

(\cfunction{initspam()} in the example), but will not load the module

again if it was loaded from a dynamically loadable object file

(\file{.so} on \UNIX, \file{.dll} on Windows).}

A more substantial example module is included in the Python source

distribution as \file{Modules/xxmodule.c}. This file may be used as a

template or simply read as an example. The \program{modulator.py}

script included in the source distribution or Windows install provides

a simple graphical user interface for declaring the functions and

objects which a module should implement, and can generate a template

which can be filled in. The script lives in the

\file{Tools/modulator/} directory; see the \file{README} file there

for more information.

\section{Compilation and Linkage

\label{compilation}}

There are two more things to do before you can use your new extension:

compiling and linking it with the Python system. If you use dynamic

loading, the details may depend on the style of dynamic loading your

system uses; see the chapters about building extension modules

(chapter \ref{building}) and additional information that pertains only

to building on Windows (chapter \ref{building-on-windows}) for more

information about this.

If you can't use dynamic loading, or if you want to make your module a

permanent part of the Python interpreter, you will have to change the

configuration setup and rebuild the interpreter. Luckily, this is

very simple on \UNIX: just place your file (\file{spammodule.c} for

example) in the \file{Modules/} directory of an unpacked source

distribution, add a line to the file \file{Modules/Setup.local}

describing your file:

\begin{verbatim}

spam spammodule.o

\end{verbatim}

and rebuild the interpreter by running \program{make} in the toplevel

directory. You can also run \program{make} in the \file{Modules/}

subdirectory, but then you must first rebuild \file{Makefile}

there by running `\program{make} Makefile'. (This is necessary each

time you change the \file{Setup} file.)

If your module requires additional libraries to link with, these can

be listed on the line in the configuration file as well, for instance:

\begin{verbatim}

spam spammodule.o -lX11

\end{verbatim}

\section{Calling Python Functions from C

\label{callingPython}}

So far we have concentrated on making C functions callable from

Python. The reverse is also useful: calling Python functions from C.

This is especially the case for libraries that support so-called

``callback'' functions. If a C interface makes use of callbacks, the

equivalent Python often needs to provide a callback mechanism to the

Python programmer; the implementation will require calling the Python

callback functions from a C callback. Other uses are also imaginable.

Fortunately, the Python interpreter is easily called recursively, and

there is a standard interface to call a Python function. (I won't

dwell on how to call the Python parser with a particular string as

input --- if you're interested, have a look at the implementation of

the \programopt{-c} command line option in \file{Python/pythonmain.c}

from the Python source code.)

Calling a Python function is easy. First, the Python program must

somehow pass you the Python function object. You should provide a

function (or some other interface) to do this. When this function is

called, save a pointer to the Python function object (be careful to

\cfunction{Py_INCREF()} it!) in a global variable --- or wherever you

see fit. For example, the following function might be part of a module

definition:

\begin{verbatim}

static PyObject *my_callback = NULL;

static PyObject *

my_set_callback(PyObject *dummy, PyObject *args)

{

PyObject *result = NULL;

PyObject *temp;

if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {

if (!PyCallable_Check(temp)) {

PyErr_SetString(PyExc_TypeError, "parameter must be callable");

return NULL;

}

Py_XINCREF(temp); /* Add a reference to new callback */

Py_XDECREF(my_callback); /* Dispose of previous callback */

my_callback = temp; /* Remember new callback */

/* Boilerplate to return "None" */

Py_INCREF(Py_None);

result = Py_None;

}

return result;

}

\end{verbatim}

This function must be registered with the interpreter using the

\constant{METH_VARARGS} flag; this is described in section

\ref{methodTable}, ``The Module's Method Table and Initialization

Function.'' The \cfunction{PyArg_ParseTuple()} function and its

arguments are documented in section~\ref{parseTuple}, ``Extracting

Parameters in Extension Functions.''

The macros \cfunction{Py_XINCREF()} and \cfunction{Py_XDECREF()}

increment/decrement the reference count of an object and are safe in

the presence of \NULL{} pointers (but note that \var{temp} will not be

\NULL{} in this context). More info on them in

section~\ref{refcounts}, ``Reference Counts.''

Later, when it is time to call the function, you call the C function

\cfunction{PyEval_CallObject()}.\ttindex{PyEval_CallObject()} This

function has two arguments, both pointers to arbitrary Python objects:

the Python function, and the argument list. The argument list must

always be a tuple object, whose length is the number of arguments. To

call the Python function with no arguments, pass an empty tuple; to

call it with one argument, pass a singleton tuple.

\cfunction{Py_BuildValue()} returns a tuple when its format string

consists of zero or more format codes between parentheses. For

example:

\begin{verbatim}

int arg;

PyObject *arglist;

PyObject *result;

...

arg = 123;

...

/* Time to call the callback */

arglist = Py_BuildValue("(i)", arg);

result = PyEval_CallObject(my_callback, arglist);

Py_DECREF(arglist);

\end{verbatim}

\cfunction{PyEval_CallObject()} returns a Python object pointer: this is

the return value of the Python function. \cfunction{PyEval_CallObject()} is

``reference-count-neutral'' with respect to its arguments. In the

example a new tuple was created to serve as the argument list, which

is \cfunction{Py_DECREF()}-ed immediately after the call.

The return value of \cfunction{PyEval_CallObject()} is ``new'': either it

is a brand new object, or it is an existing object whose reference

count has been incremented. So, unless you want to save it in a

global variable, you should somehow \cfunction{Py_DECREF()} the result,

even (especially!) if you are not interested in its value.

Before you do this, however, it is important to check that the return

value isn't \NULL. If it is, the Python function terminated by

raising an exception. If the C code that called

\cfunction{PyEval_CallObject()} is called from Python, it should now

return an error indication to its Python caller, so the interpreter

can print a stack trace, or the calling Python code can handle the

exception. If this is not possible or desirable, the exception should

be cleared by calling \cfunction{PyErr_Clear()}. For example:

\begin{verbatim}

if (result == NULL)

return NULL; /* Pass error back */

...use result...

Py_DECREF(result);

\end{verbatim}

Depending on the desired interface to the Python callback function,

you may also have to provide an argument list to

\cfunction{PyEval_CallObject()}. In some cases the argument list is

also provided by the Python program, through the same interface that

specified the callback function. It can then be saved and used in the

same manner as the function object. In other cases, you may have to

construct a new tuple to pass as the argument list. The simplest way

to do this is to call \cfunction{Py_BuildValue()}. For example, if

you want to pass an integral event code, you might use the following

code:

\begin{verbatim}

PyObject *arglist;

...

arglist = Py_BuildValue("(l)", eventcode);

result = PyEval_CallObject(my_callback, arglist);

Py_DECREF(arglist);

if (result == NULL)

return NULL; /* Pass error back */

/* Here maybe use the result */

Py_DECREF(result);

\end{verbatim}

Note the placement of \samp{Py_DECREF(arglist)} immediately after the

call, before the error check! Also note that strictly spoken this

code is not complete: \cfunction{Py_BuildValue()} may run out of

memory, and this should be checked.

\section{Extracting Parameters in Extension Functions

\label{parseTuple}}

\ttindex{PyArg_ParseTuple()}

The \cfunction{PyArg_ParseTuple()} function is declared as follows:

\begin{verbatim}

int PyArg_ParseTuple(PyObject *arg, char *format, ...);

\end{verbatim}

The \var{arg} argument must be a tuple object containing an argument

list passed from Python to a C function. The \var{format} argument

must be a format string, whose syntax is explained in

``\ulink{Parsing arguments and building

values}{../api/arg-parsing.html}'' in the

\citetitle[../api/api.html]{Python/C API Reference Manual}. The

remaining arguments must be addresses of variables whose type is

determined by the format string.

Note that while \cfunction{PyArg_ParseTuple()} checks that the Python

arguments have the required types, it cannot check the validity of the

addresses of C variables passed to the call: if you make mistakes

there, your code will probably crash or at least overwrite random bits

in memory. So be careful!

Note that any Python object references which are provided to the

caller are \emph{borrowed} references; do not decrement their

reference count!

Some example calls:

\begin{verbatim}

int ok;

int i, j;

long k, l;

const char *s;

int size;

ok = PyArg_ParseTuple(args, ""); /* No arguments */

/* Python call: f() */

\end{verbatim}

\begin{verbatim}

ok = PyArg_ParseTuple(args, "s", &s); /* A string */

/* Possible Python call: f('whoops!') */

\end{verbatim}

\begin{verbatim}

ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */

/* Possible Python call: f(1, 2, 'three') */

\end{verbatim}

\begin{verbatim}

ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);

/* A pair of ints and a string, whose size is also returned */

/* Possible Python call: f((1, 2), 'three') */

\end{verbatim}

\begin{verbatim}

{

const char *file;

const char *mode = "r";

int bufsize = 0;

ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);

/* A string, and optionally another string and an integer */

/* Possible Python calls:

f('spam')

f('spam', 'w')

f('spam', 'wb', 100000) */

}

\end{verbatim}

\begin{verbatim}

{

int left, top, right, bottom, h, v;

ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",

&left, &top, &right, &bottom, &h, &v);

/* A rectangle and a point */

/* Possible Python call:

f(((0, 0), (400, 300)), (10, 10)) */

}

\end{verbatim}

\begin{verbatim}

{

Py_complex c;

ok = PyArg_ParseTuple(args, "D:myfunction", &c);

/* a complex, also providing a function name for errors */

/* Possible Python call: myfunction(1+2j) */

}

\end{verbatim}

\section{Keyword Parameters for Extension Functions

\label{parseTupleAndKeywords}}

\ttindex{PyArg_ParseTupleAndKeywords()}

The \cfunction{PyArg_ParseTupleAndKeywords()} function is declared as

follows:

\begin{verbatim}

int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,

char *format, char *kwlist[], ...);

\end{verbatim}

The \var{arg} and \var{format} parameters are identical to those of the

\cfunction{PyArg_ParseTuple()} function. The \var{kwdict} parameter

is the dictionary of keywords received as the third parameter from the

Python runtime. The \var{kwlist} parameter is a \NULL-terminated

list of strings which identify the parameters; the names are matched

with the type information from \var{format} from left to right. On

success, \cfunction{PyArg_ParseTupleAndKeywords()} returns true,

otherwise it returns false and raises an appropriate exception.

\note{Nested tuples cannot be parsed when using keyword

arguments! Keyword parameters passed in which are not present in the

\var{kwlist} will cause \exception{TypeError} to be raised.}

Here is an example module which uses keywords, based on an example by

Geoff Philbrick (\email{philbrick@hks.com}):%

\index{Philbrick, Geoff}

\begin{verbatim}

#include "Python.h"

static PyObject *

keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)

{

int voltage;

char *state = "a stiff";

char *action = "voom";

char *type = "Norwegian Blue";

static char *kwlist[] = {"voltage", "state", "action", "type", NULL};

if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,

&voltage, &state, &action, &type))

return NULL;

printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",

action, voltage);

printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);

Py_INCREF(Py_None);

return Py_None;

}

static PyMethodDef keywdarg_methods[] = {

/* The cast of the function is necessary since PyCFunction values

* only take two PyObject* parameters, and keywdarg_parrot() takes

* three.

*/

{"parrot", (PyCFunction)keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,

"Print a lovely skit to standard output."},

{NULL, NULL, 0, NULL} /* sentinel */

};

\end{verbatim}

\begin{verbatim}

void

initkeywdarg(void)

{

/* Create the module and add the functions */

Py_InitModule("keywdarg", keywdarg_methods);

}

\end{verbatim}

\section{Building Arbitrary Values

\label{buildValue}}

This function is the counterpart to \cfunction{PyArg_ParseTuple()}. It is

declared as follows:

\begin{verbatim}

PyObject *Py_BuildValue(char *format, ...);

\end{verbatim}

It recognizes a set of format units similar to the ones recognized by

\cfunction{PyArg_ParseTuple()}, but the arguments (which are input to the

function, not output) must not be pointers, just values. It returns a

new Python object, suitable for returning from a C function called

from Python.

One difference with \cfunction{PyArg_ParseTuple()}: while the latter

requires its first argument to be a tuple (since Python argument lists

are always represented as tuples internally),

\cfunction{Py_BuildValue()} does not always build a tuple. It builds

a tuple only if its format string contains two or more format units.

If the format string is empty, it returns \code{None}; if it contains

exactly one format unit, it returns whatever object is described by

that format unit. To force it to return a tuple of size 0 or one,

parenthesize the format string.

Examples (to the left the call, to the right the resulting Python value):

\begin{verbatim}

Py_BuildValue("") None

Py_BuildValue("i", 123) 123

Py_BuildValue("iii", 123, 456, 789) (123, 456, 789)

Py_BuildValue("s", "hello") 'hello'

Py_BuildValue("ss", "hello", "world") ('hello', 'world')

Py_BuildValue("s#", "hello", 4) 'hell'

Py_BuildValue("()") ()

Py_BuildValue("(i)", 123) (123,)

Py_BuildValue("(ii)", 123, 456) (123, 456)

Py_BuildValue("(i,i)", 123, 456) (123, 456)

Py_BuildValue("[i,i]", 123, 456) [123, 456]

Py_BuildValue("{s:i,s:i}",

"abc", 123, "def", 456) {'abc': 123, 'def': 456}

Py_BuildValue("((ii)(ii)) (ii)",

1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6))

\end{verbatim}

\section{Reference Counts

\label{refcounts}}

In languages like C or \Cpp, the programmer is responsible for

dynamic allocation and deallocation of memory on the heap. In C,

this is done using the functions \cfunction{malloc()} and

\cfunction{free()}. In \Cpp, the operators \keyword{new} and

\keyword{delete} are used with essentially the same meaning and

we'll restrict the following discussion to the C case.

Every block of memory allocated with \cfunction{malloc()} should

eventually be returned to the pool of available memory by exactly one

call to \cfunction{free()}. It is important to call

\cfunction{free()} at the right time. If a block's address is

forgotten but \cfunction{free()} is not called for it, the memory it

occupies cannot be reused until the program terminates. This is

called a \dfn{memory leak}. On the other hand, if a program calls

\cfunction{free()} for a block and then continues to use the block, it

creates a conflict with re-use of the block through another

\cfunction{malloc()} call. This is called \dfn{using freed memory}.

It has the same bad consequences as referencing uninitialized data ---

core dumps, wrong results, mysterious crashes.

Common causes of memory leaks are unusual paths through the code. For

instance, a function may allocate a block of memory, do some

calculation, and then free the block again. Now a change in the

requirements for the function may add a test to the calculation that

detects an error condition and can return prematurely from the

function. It's easy to forget to free the allocated memory block when

taking this premature exit, especially when it is added later to the

code. Such leaks, once introduced, often go undetected for a long

time: the error exit is taken only in a small fraction of all calls,

and most modern machines have plenty of virtual memory, so the leak

only becomes apparent in a long-running process that uses the leaking

function frequently. Therefore, it's important to prevent leaks from

happening by having a coding convention or strategy that minimizes

this kind of errors.

Since Python makes heavy use of \cfunction{malloc()} and

\cfunction{free()}, it needs a strategy to avoid memory leaks as well

as the use of freed memory. The chosen method is called

\dfn{reference counting}. The principle is simple: every object

contains a counter, which is incremented when a reference to the

object is stored somewhere, and which is decremented when a reference

to it is deleted. When the counter reaches zero, the last reference

to the object has been deleted and the object is freed.

An alternative strategy is called \dfn{automatic garbage collection}.

(Sometimes, reference counting is also referred to as a garbage

collection strategy, hence my use of ``automatic'' to distinguish the

two.) The big advantage of automatic garbage collection is that the

user doesn't need to call \cfunction{free()} explicitly. (Another claimed

advantage is an improvement in speed or memory usage --- this is no

hard fact however.) The disadvantage is that for C, there is no

truly portable automatic garbage collector, while reference counting

can be implemented portably (as long as the functions \cfunction{malloc()}

and \cfunction{free()} are available --- which the C Standard guarantees).

Maybe some day a sufficiently portable automatic garbage collector

will be available for C. Until then, we'll have to live with

reference counts.

While Python uses the traditional reference counting implementation,

it also offers a cycle detector that works to detect reference

cycles. This allows applications to not worry about creating direct

or indirect circular references; these are the weakness of garbage

collection implemented using only reference counting. Reference

cycles consist of objects which contain (possibly indirect) references

to themselves, so that each object in the cycle has a reference count

which is non-zero. Typical reference counting implementations are not

able to reclaim the memory belonging to any objects in a reference

cycle, or referenced from the objects in the cycle, even though there

are no further references to the cycle itself.

The cycle detector is able to detect garbage cycles and can reclaim

them so long as there are no finalizers implemented in Python

(\method{__del__()} methods). When there are such finalizers, the

detector exposes the cycles through the \ulink{\module{gc}

module}{../lib/module-gc.html} (specifically, the \code{garbage}

variable in that module). The \module{gc} module also exposes a way

to run the detector (the \function{collect()} function), as well as

configuration interfaces and the ability to disable the detector at

runtime. The cycle detector is considered an optional component;

though it is included by default, it can be disabled at build time

using the \longprogramopt{without-cycle-gc} option to the

\program{configure} script on \UNIX{} platforms (including Mac OS X)

or by removing the definition of \code{WITH_CYCLE_GC} in the

\file{pyconfig.h} header on other platforms. If the cycle detector is

disabled in this way, the \module{gc} module will not be available.

\subsection{Reference Counting in Python

\label{refcountsInPython}}

There are two macros, \code{Py_INCREF(x)} and \code{Py_DECREF(x)},

which handle the incrementing and decrementing of the reference count.

\cfunction{Py_DECREF()} also frees the object when the count reaches zero.

For flexibility, it doesn't call \cfunction{free()} directly --- rather, it

makes a call through a function pointer in the object's \dfn{type

object}. For this purpose (and others), every object also contains a

pointer to its type object.

The big question now remains: when to use \code{Py_INCREF(x)} and

\code{Py_DECREF(x)}? Let's first introduce some terms. Nobody

``owns'' an object; however, you can \dfn{own a reference} to an

object. An object's reference count is now defined as the number of

owned references to it. The owner of a reference is responsible for

calling \cfunction{Py_DECREF()} when the reference is no longer

needed. Ownership of a reference can be transferred. There are three

ways to dispose of an owned reference: pass it on, store it, or call

\cfunction{Py_DECREF()}. Forgetting to dispose of an owned reference

creates a memory leak.

It is also possible to \dfn{borrow}\footnote{The metaphor of

``borrowing'' a reference is not completely correct: the owner still

has a copy of the reference.} a reference to an object. The borrower

of a reference should not call \cfunction{Py_DECREF()}. The borrower must

not hold on to the object longer than the owner from which it was

borrowed. Using a borrowed reference after the owner has disposed of

it risks using freed memory and should be avoided

completely.\footnote{Checking that the reference count is at least 1

\strong{does not work} --- the reference count itself could be in

freed memory and may thus be reused for another object!}

The advantage of borrowing over owning a reference is that you don't

need to take care of disposing of the reference on all possible paths

through the code --- in other words, with a borrowed reference you

don't run the risk of leaking when a premature exit is taken. The

disadvantage of borrowing over leaking is that there are some subtle

situations where in seemingly correct code a borrowed reference can be

used after the owner from which it was borrowed has in fact disposed

of it.

A borrowed reference can be changed into an owned reference by calling

\cfunction{Py_INCREF()}. This does not affect the status of the owner from

which the reference was borrowed --- it creates a new owned reference,

and gives full owner responsibilities (the new owner must

dispose of the reference properly, as well as the previous owner).

\subsection{Ownership Rules

\label{ownershipRules}}

Whenever an object reference is passed into or out of a function, it

is part of the function's interface specification whether ownership is

transferred with the reference or not.

Most functions that return a reference to an object pass on ownership

with the reference. In particular, all functions whose function it is

to create a new object, such as \cfunction{PyInt_FromLong()} and

\cfunction{Py_BuildValue()}, pass ownership to the receiver. Even if

the object is not actually new, you still receive ownership of a new

reference to that object. For instance, \cfunction{PyInt_FromLong()}

maintains a cache of popular values and can return a reference to a

cached item.

Many functions that extract objects from other objects also transfer

ownership with the reference, for instance

\cfunction{PyObject_GetAttrString()}. The picture is less clear, here,

however, since a few common routines are exceptions:

\cfunction{PyTuple_GetItem()}, \cfunction{PyList_GetItem()},

\cfunction{PyDict_GetItem()}, and \cfunction{PyDict_GetItemString()}

all return references that you borrow from the tuple, list or

dictionary.

The function \cfunction{PyImport_AddModule()} also returns a borrowed

reference, even though it may actually create the object it returns:

this is possible because an owned reference to the object is stored in

\code{sys.modules}.

When you pass an object reference into another function, in general,

the function borrows the reference from you --- if it needs to store

it, it will use \cfunction{Py_INCREF()} to become an independent

owner. There are exactly two important exceptions to this rule:

\cfunction{PyTuple_SetItem()} and \cfunction{PyList_SetItem()}. These

functions take over ownership of the item passed to them --- even if

they fail! (Note that \cfunction{PyDict_SetItem()} and friends don't

take over ownership --- they are ``normal.'')

When a C function is called from Python, it borrows references to its

arguments from the caller. The caller owns a reference to the object,

so the borrowed reference's lifetime is guaranteed until the function