1. Étendre Python en C ou C++
*****************************

Il est relativement facile d’ajouter de nouveaux modules à Python, si
vous savez programmer en C. Ces *<modules d’extension> extension
modules* permettent deux choses qui ne sont pas possible directement
en Python: Elles peuvent définir de nouveaux types natifs, et peuvent
appeler des fonctions de bibliothèques C ou appels systèmes.

Pour gérer les extensions, l’API Python (*Application Programmer
Interface*) définit un ensemble de fonctions, macros et variables qui
donnent accès à la plupart des aspects du système d’exécution de
Python. L’API Python est incorporée dans un fichier source C en
incluant l’en-tête ""Python.h"".

La compilation d’un module d’extension dépend de l’usage prévu et de
la configuration du système, plus de détails peuvent être trouvés dans
les chapitres suivants.

Note: L’interface d’extension C est spécifique à CPython, et les
  modules d’extension ne fonctionne pas sur les autres implémentations
  de Python. Dans de nombreux cas, il est possible d’éviter la
  rédaction des extensions en C et ainsi préserver la portabilité vers
  d’autres implémentations. Par exemple, si vous devez appeler une
  fonction de la bibliothèque C ou faire un appel système, vous
  devriez envisager d’utiliser le module "ctypes" ou d’utiliser la
  bibliothèque cffi plutôt que d’écrire du code C sur mesure. Ces
  modules vous permettent d’écrire du code Python s’interfaçant avec
  le code C et sont plus portables entre les implémentations de Python
  que l’écriture et la compilation d’une d’extension C.


1.1. Un exemple simple
======================

Let’s create an extension module called "spam" (the favorite food of
Monty Python fans…) and let’s say we want to create a Python interface
to the C library function "system()" [1]. 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:

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

Commencez par créer un fichier "spammodule.c". (Historiquement, si un
module se nomme "spam", le fichier C contenant son implémentation est
appelé "spammodule.c". Si le nom du module est très long, comme
"spammify", le nom du module peut être juste "spammify.c".)

La première ligne de notre fichier peut être :

   #include <Python.h>

qui récupère l’API Python (vous pouvez ajouter un commentaire
décrivant le but du module et un avis de droit d’auteur si vous le
souhaitez).

Note: Python pouvant définir certaines définitions pré-processeur
  qui affectent les têtes standard sur certains systèmes, vous *devez*
  inclure "Python.h" avant les en-têtes standards.

Tous les symboles exposés par "Python.h" sont préfixés de "Py" ou
"PY", sauf ceux qui sont définis dans les en-têtes standard. Pour le
confort, et comme ils sont largement utilisés par l’interpréteur
Python, ""Python.h"" inclut lui même quelques d’en-têtes standard :
"<stdio.h>", "<string.h>", "<errno.h>" et "<stdlib.h>". Si ce dernier
n’existe pas sur votre système, il déclare les fonctions "malloc()",
"free()" et "realloc()" directement.

La prochaine chose que nous ajoutons à notre fichier de module est la
fonction C qui sera appelée lorsque l’expression Python
"spam.system(chaîne)" sera évaluée (nous verrons bientôt comment elle
finit par être appelée) :

   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);
   }

Il y a une correspondance directe de la liste des arguments en Python
(par exemple, l’expression ""ls -l"") aux arguments passés à la
fonction C. La fonction C a toujours deux arguments, appelés par
convention *self* et *args*.

For module functions, the *self* argument is *NULL* or a pointer
selected while initializing the module (see "Py_InitModule4()").  For
a method, it would point to the object instance.

L’argument *args* sera un pointeur vers un *tuple* Python contenant
les arguments. Chaque élément du *tuple* correspond à un argument dans
la liste des arguments de l’appel. Les arguments sont des objets
Python — afin d’en faire quelque chose dans notre fonction C, nous
devons les convertir en valeurs C. La fonction "PyArg_ParseTuple()" de
l’API Python vérifie les types des arguments et les convertit en
valeurs C. Elle utilise un modèle sous forme de chaîne pour déterminer
les types requis des arguments ainsi que les types de variables C dans
lequel stocker les valeurs converties. Nous en verront plus, plus
tard.

"PyArg_ParseTuple()" renvoie vrai (pas zéro) si tous les arguments ont
le bon type et que ses composants ont été stockés dans les variables
dont les adresses données. Il renvoie faux (zéro) si une liste
d’arguments invalide a été passée. Dans ce dernier cas, elle lève
également une exception appropriée de sorte que la fonction d’appel
puisse renvoyer *NULL* immédiatement (comme nous l’avons vu dans
l’exemple).


1.2. Intermezzo: Les erreurs et exceptions
==========================================

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 "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 "sys.exc_type",
"sys.exc_value" and "sys.exc_traceback" (see the section on module
"sys" in the Python Library Reference).  It is important to know about
them to understand how errors are passed around.

L’API Python définit un certain nombre de fonctions pour créer
différents types d’exceptions.

La plus courante est "PyErr_SetString()". Ses arguments sont un objet
exception et une chaîne C. L’objet exception est généralement un objet
prédéfini comme "PyExc_ZeroDivisionError". La chaîne C indique la
cause de l’erreur et est convertie en une chaîne Python puis stockée
en tant que « valeur associée » à l’exception.

Une autre fonction utile est "PyErr_SetFromErrno()", qui construit une
exception à partir de la valeur de la variable globale "errno". La
fonction la plus générale est "PyErr_SetObject()", qui prend deux
arguments: l’exception et sa valeur associée. Vous ne devez pas
appliquer "Py_INCREF()" aux objets transmis à ces fonctions.

Vous pouvez tester de manière non destructive si une exception a été
levée avec "PyErr_Occurred()". Cela renvoie l’objet exception actuel,
ou *NULL* si aucune exception n’a eu lieu. Cependant, vous ne devriez
pas avoir besoin d’appeler "PyErr_Occurred()" pour voir si une erreur
est survenue durant l’appel d’une fonction, puisque vous devriez être
en mesure de le déterminer à partir de la valeur de retour.

Lorsqu’une fonction *f* ayant appelé une autre fonction *g* détecte
que cette dernière a échoué, *f* devrait donner une valeur d’erreur à
son tour (habituellement *NULL* ou "-1"). *f* ne devrait *pas* appeler
l’une des fonctions "PyErr_*()", l’une d’elles ayant déjà été appelée
par *g*. La fonction appelant *f* est alors censée renvoyer aussi un
code d’erreur à celle qui l’a appelée, toujours sans utiliser
"PyErr_*()", et ainsi de suite. La cause la plus détaillée de l’erreur
a déjà été signalée par la fonction l’ayant détectée en premier. Une
fois l’erreur remontée à la boucle principale de l’interpréteur
Python, il interrompt le code en cours d’exécution et essaie de
trouver un gestionnaire d’exception spécifié par le développeur
Python.

(Il y a des situations où un module peut effectivement donner un
message d’erreur plus détaillé en appelant une autre fonction
"PyErr_*()", dans de tels cas, il est tout à fait possible de le
faire. Cependant, ce n’est généralement pas nécessaire, et peut amener
à perdre des informations sur la cause de l’erreur: la plupart des
opérations peuvent échouer pour tout un tas de raisons).

Pour ignorer une exception qui aurait été émise lors d’un appel de
fonction qui aurait échoué, l’exception doit être retirée
explicitement en appelant "PyErr_Clear()". Le seul cas pour lequel du
code C devrait appeler "PyErr_Clear()" est lorsqu’il ne veut pas
passer l’erreur à l’interpréteur, mais souhaite la gérer lui-même
(peut-être en essayant quelque chose d’autre, ou en prétendant que
rien n’a mal tourné).

Every failing "malloc()" call must be turned into an exception — the
direct caller of "malloc()" (or "realloc()") must call
"PyErr_NoMemory()" and return a failure indicator itself.  All the
object-creating functions (for example, "PyInt_FromLong()") already do
this, so this note is only relevant to those who call "malloc()"
directly.

Notez également que, à l’exception notable de "PyArg_ParseTuple()" et
compagnie, les fonctions qui renvoient leur statut sous forme d’entier
donnent généralement une valeur positive ou zéro en cas de succès et
"-1" en cas d’échec, comme les appels du système Unix.

Enfin, lorsque vous renvoyez un code d’erreur, n’oubliez pas faire un
brin de nettoyage (en appelant "Py_XDECREF()" ou "Py_DECREF()" avec
les objets que vous auriez déjà créés) !

Le choix de l’exception à lever vous incombe. Il existe des objets C
correspondant à chaque exception Python, tel que
"PyExc_ZeroDivisionError", que vous pouvez utiliser directement.
Choisissez judicieusement vos exceptions, typiquement n’utilisez pas
"PyExc_TypeError" pour indiquer qu’un fichier n’a pas pu être ouvert
(qui devrait probablement être "PyExc_IOError"). Si quelque chose ne
va pas avec la liste des arguments, la fonction "PyArg_ParseTuple()"
lève habituellement une exception "PyExc_TypeError". Mais si vous avez
un argument dont la valeur doit être dans un intervalle particulier ou
qui doit satisfaire d’autres conditions, "PyExc_ValueError" sera plus
appropriée.

Vous pouvez également créer une exception spécifique à votre module.
Pour cela, déclarez simplement une variable statique au début de votre
fichier :

   static PyObject *SpamError;

and initialize it in your module’s initialization function
("initspam()") with an exception object (leaving out the error
checking for now):

   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);
   }

Notez que le nom de exception, côté Python, est "spam.error". La
fonction "PyErr_NewException()" peut créer une classe héritant de
"Exception" (à moins qu’une autre classe ne lui soit fournie à la
place de *NULL*), voir Exceptions natives.

Notez également que la variable "SpamError" contient une référence à
la nouvelle classe créée; ceci est intentionnel! Comme l’exception
peut être retirée du module par un code externe, une référence à la
classe est nécessaire pour assurer qu’il ne sera pas rejeté, causant
"SpamError" à devenir un pointeur défaillant. S’il devenait un
pointeur défaillant, le C code qui lève l’exception peut engendrer un
rejet central ou des effets secondaires inattendus.

Nous traiterons de l’utilisation de "PyMODINIT_FUNC" comme un type de
retour de fonction plus tard dans cette section.

L’exception "spam.error" peut être levée dans votre module d’extension
en appelant "PyErr_SetString()" comme montré ci-dessous :

   static PyObject *
   spam_system(PyObject *self, PyObject *args)
   {
       const char *command;
       int sts;

       if (!PyArg_ParseTuple(args, "s", &command))
           return NULL;
       sts = system(command);
       if (sts < 0) {
           PyErr_SetString(SpamError, "System command failed");
           return NULL;
       }
       return PyLong_FromLong(sts);
   }


1.3. Retour vers l’exemple
==========================

En revenant vers notre fonction exemple, vous devriez maintenant être
capable de comprendre cette affirmation :

   if (!PyArg_ParseTuple(args, "s", &command))
       return NULL;

Elle renvoie *NULL* (l’indicateur d’erreur pour les fonctions
renvoyant des pointeurs d’objet) si une erreur est détectée dans la
liste des arguments,se fiant à l’exception définie par
"PyArg_ParseTuple()". Autrement,la valeur chaîne de l’argument a été
copiée dans la variable locale "command". Il s’agit d’une attribution
de pointeur et vous n’êtes pas supposés modifier la chaîne qui vers
laquelle il pointe (donc en C Standard, la variable "command" doit
être clairement déclarée comme "const char *command").

La prochaine instruction est un appel à la fonction Unix "system()",
en lui passant la chaîne que nous venons d’obtenir à partir de
"PyArg_ParseTuple()" :

   sts = system(command);

Our "spam.system()" function must return the value of "sts" as a
Python object.  This is done using the function "Py_BuildValue()",
which is something like the inverse of "PyArg_ParseTuple()": it takes
a format string and an arbitrary number of C values, and returns a new
Python object. More info on "Py_BuildValue()" is given later.

   return Py_BuildValue("i", sts);

Dans ce cas, elle renverra un objet entier. (Oui, même les entiers
sont des objets dans le tas en Python!)

Si vous avez une fonction C qui ne renvoie aucun argument utile (une
fonction renvoyant "void"), la fonction Python correspondante doit
renvoyer "None". Vous aurez besoin de cette locution pour cela (qui
est implémentée par la macro "Py_RETURN_NONE") :

   Py_INCREF(Py_None);
   return Py_None;

"Py_None" est le nom C pour l’objet spécial Python "None". C’est un
authentique objet Python plutôt qu’un pointeur *NULL*, qui signifie
qu’une erreur est survenue, dans la plupart des situations, comme nous
l’avons vu.


1.4. The Module’s Method Table and Initialization Function
==========================================================

I promised to show how "spam_system()" is called from Python programs.
First, we need to list its name and address in a « method table »:

   static PyMethodDef SpamMethods[] = {
       ...
       {"system",  spam_system, METH_VARARGS,
        "Execute a shell command."},
       ...
       {NULL, NULL, 0, NULL}        /* Sentinel */
   };

Note the third entry ("METH_VARARGS").  This is a flag telling the
interpreter the calling convention to be used for the C function.  It
should normally always be "METH_VARARGS" or "METH_VARARGS |
METH_KEYWORDS"; a value of "0" means that an obsolete variant of
"PyArg_ParseTuple()" is used.

When using only "METH_VARARGS", the function should expect the Python-
level parameters to be passed in as a tuple acceptable for parsing via
"PyArg_ParseTuple()"; more information on this function is provided
below.

The "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 "PyObject *" parameter which will be a
dictionary of keywords. Use "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
"initname()", where *name* is the name of the module, and should be
the only non-"static" item defined in the module file:

   PyMODINIT_FUNC
   initspam(void)
   {
       (void) Py_InitModule("spam", SpamMethods);
   }

Note that PyMODINIT_FUNC declares the function as "void" return type,
declares any special linkage declarations required by the platform,
and for  C++ declares the function as "extern "C"".

When the Python program imports module "spam" for the first time,
"initspam()" is called. (See below for comments about embedding
Python.) It calls "Py_InitModule()", which creates a « module object »
(which is inserted in the dictionary "sys.modules" under the key
""spam""), and inserts built-in function objects into the newly
created module based upon the table (an array of "PyMethodDef"
structures) that was passed as its second argument. "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 "initspam()" function is not called
automatically unless there’s an entry in the "_PyImport_Inittab"
table. The easiest way to handle this is to statically initialize your
statically-linked modules by directly calling "initspam()" after the
call to "Py_Initialize()":

   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();

       ...

An example may be found in the file "Demo/embed/demo.c" in the Python
source distribution.

Note: Removing entries from "sys.modules" or importing compiled
  modules into multiple interpreters within a process (or following a
  "fork()" without an intervening "exec()") can create problems for
  some extension modules. Extension module authors should exercise
  caution when initializing internal data structures. Note also that
  the "reload()" function can be used with extension modules, and will
  call the module initialization function ("initspam()" in the
  example), but will not load the module again if it was loaded from a
  dynamically loadable object file (".so" on Unix, ".dll" on Windows).

A more substantial example module is included in the Python source
distribution as "Modules/xxmodule.c".  This file may be used as a
template or simply read as an example.


1.5. Compilation and Linkage
============================

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 Construire les extensions C et C++ avec distutils) and
additional information that pertains only to building on Windows
(chapter Construire des extensions C et C++ sur 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 ("spammodule.c" for example)
in the "Modules/" directory of an unpacked source distribution, add a
line to the file "Modules/Setup.local" describing your file:

   spam spammodule.o

and rebuild the interpreter by running **make** in the toplevel
directory.  You can also run **make** in the "Modules/" subdirectory,
but then you must first rebuild "Makefile" there by running “**make**
Makefile”.  (This is necessary each time you change the "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:

   spam spammodule.o -lX11


1.6. Calling Python Functions from C
====================================

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
"-c" command line option in "Modules/main.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
"Py_INCREF()" it!) in a global variable — or wherever you see fit. For
example, the following function might be part of a module definition:

   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;
   }

This function must be registered with the interpreter using the
"METH_VARARGS" flag; this is described in section The Module’s Method
Table and Initialization Function.  The "PyArg_ParseTuple()" function
and its arguments are documented in section Extracting Parameters in
Extension Functions.

The macros "Py_XINCREF()" and "Py_XDECREF()" increment/decrement the
reference count of an object and are safe in the presence of *NULL*
pointers (but note that *temp* will not be  *NULL* in this context).
More info on them in section Reference Counts.

Later, when it is time to call the function, you call the C function
"PyObject_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 in NULL, or an empty tuple; to call it with one
argument, pass a singleton tuple. "Py_BuildValue()" returns a tuple
when its format string consists of zero or more format codes between
parentheses.  For example:

   int arg;
   PyObject *arglist;
   PyObject *result;
   ...
   arg = 123;
   ...
   /* Time to call the callback */
   arglist = Py_BuildValue("(i)", arg);
   result = PyObject_CallObject(my_callback, arglist);
   Py_DECREF(arglist);

"PyObject_CallObject()" returns a Python object pointer: this is the
return value of the Python function.  "PyObject_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 "Py_DECREF()"-ed immediately after the "PyObject_CallObject()"
call.

The return value of "PyObject_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 "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
"PyObject_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 "PyErr_Clear()".  For example:

   if (result == NULL)
       return NULL; /* Pass error back */
   ...use result...
   Py_DECREF(result);

Depending on the desired interface to the Python callback function,
you may also have to provide an argument list to
"PyObject_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 "Py_BuildValue()".  For example, if you want to
pass an integral event code, you might use the following code:

   PyObject *arglist;
   ...
   arglist = Py_BuildValue("(l)", eventcode);
   result = PyObject_CallObject(my_callback, arglist);
   Py_DECREF(arglist);
   if (result == NULL)
       return NULL; /* Pass error back */
   /* Here maybe use the result */
   Py_DECREF(result);

Note the placement of "Py_DECREF(arglist)" immediately after the call,
before the error check!  Also note that strictly speaking this code is
not complete: "Py_BuildValue()" may run out of memory, and this should
be checked.

You may also call a function with keyword arguments by using
"PyObject_Call()", which supports arguments and keyword arguments.  As
in the above example, we use "Py_BuildValue()" to construct the
dictionary.

   PyObject *dict;
   ...
   dict = Py_BuildValue("{s:i}", "name", val);
   result = PyObject_Call(my_callback, NULL, dict);
   Py_DECREF(dict);
   if (result == NULL)
       return NULL; /* Pass error back */
   /* Here maybe use the result */
   Py_DECREF(result);


1.7. Extracting Parameters in Extension Functions
=================================================

The "PyArg_ParseTuple()" function is declared as follows:

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

The *arg* argument must be a tuple object containing an argument list
passed from Python to a C function.  The *format* argument must be a
format string, whose syntax is explained in Analyse des arguments et
construction des valeurs in the Python/C API Reference Manual.  The
remaining arguments must be addresses of variables whose type is
determined by the format string.

Note that while "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!

Notez que n’importe quelles références sur un objet Python qui sont
données à l’appelant sont des références *empruntées* ; ne décrémentez
pas leur compteur de références !

Some example calls:

   int ok;
   int i, j;
   long k, l;
   const char *s;
   int size;

   ok = PyArg_ParseTuple(args, ""); /* No arguments */
       /* Python call: f() */

   ok = PyArg_ParseTuple(args, "s", &s); /* A string */
       /* Possible Python call: f('whoops!') */

   ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
       /* Possible Python call: f(1, 2, 'three') */

   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') */

   {
       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) */
   }

   {
       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)) */
   }

   {
       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) */
   }


1.8. Keyword Parameters for Extension Functions
===============================================

The "PyArg_ParseTupleAndKeywords()" function is declared as follows:

   int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
                                   char *format, char *kwlist[], ...);

The *arg* and *format* parameters are identical to those of the
"PyArg_ParseTuple()" function.  The *kwdict* parameter is the
dictionary of keywords received as the third parameter from the Python
runtime.  The *kwlist* parameter is a *NULL*-terminated list of
strings which identify the parameters; the names are matched with the
type information from *format* from left to right.  On success,
"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 *kwlist*
  will cause "TypeError" to be raised.

Here is an example module which uses keywords, based on an example by
Geoff Philbrick (philbrick@hks.com):

   #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 */
   };

   void
   initkeywdarg(void)
   {
     /* Create the module and add the functions */
     Py_InitModule("keywdarg", keywdarg_methods);
   }


1.9. Building Arbitrary Values
==============================

This function is the counterpart to "PyArg_ParseTuple()".  It is
declared as follows:

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

It recognizes a set of format units similar to the ones recognized by
"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 "PyArg_ParseTuple()": while the latter requires
its first argument to be a tuple (since Python argument lists are
always represented as tuples internally), "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 "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):

   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))


1.10. Reference Counts
======================

In languages like C or C++, the programmer is responsible for dynamic
allocation and deallocation of memory on the heap.  In C, this is done
using the functions "malloc()" and "free()".  In C++, the operators
"new" and "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 "malloc()" should eventually be
returned to the pool of available memory by exactly one call to
"free()". It is important to call "free()" at the right time.  If a
block’s address is forgotten but "free()" is not called for it, the
memory it occupies cannot be reused until the program terminates.
This is called a *memory leak*.  On the other hand, if a program calls
"free()" for a block and then continues to use the block, it creates a
conflict with re-use of the block through another "malloc()" call.
This is called *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 "malloc()" and "free()", it needs a
strategy to avoid memory leaks as well as the use of freed memory.
The chosen method is called *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 *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 "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 "malloc()" and "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
("__del__()" methods). When there are such finalizers, the detector
exposes the cycles through the "gc" module (specifically, the
"garbage" variable in that module). The "gc" module also exposes a way
to run the detector (the "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 "--without-cycle-gc" option to the **configure** script on
Unix platforms (including Mac OS X) or by removing the definition of
"WITH_CYCLE_GC" in the "pyconfig.h" header on other platforms.  If the
cycle detector is disabled in this way, the "gc" module will not be
available.


1.10.1. Reference Counting in Python
------------------------------------

There are two macros, "Py_INCREF(x)" and "Py_DECREF(x)", which handle
the incrementing and decrementing of the reference count.
"Py_DECREF()" also frees the object when the count reaches zero. For
flexibility, it doesn’t call "free()" directly — rather, it makes a
call through a function pointer in the object’s *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 "Py_INCREF(x)" and
"Py_DECREF(x)"? Let’s first introduce some terms.  Nobody « owns » an
object; however, you can *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 "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 "Py_DECREF()". Forgetting to
dispose of an owned reference creates a memory leak.

It is also possible to *borrow* [2] a reference to an object.  The
borrower of a reference should not call "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 [3].

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 owning 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
"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).


1.10.2. Ownership Rules
-----------------------

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 "PyInt_FromLong()" and
"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, "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 "PyObject_GetAttrString()".
The picture is less clear, here, however, since a few common routines
are exceptions: "PyTuple_GetItem()", "PyList_GetItem()",
"PyDict_GetItem()", and "PyDict_GetItemString()" all return references
that you borrow from the tuple, list or dictionary.

The function "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
"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 "Py_INCREF()" to become an independent owner.  There are
exactly two important exceptions to this rule: "PyTuple_SetItem()" and
"PyList_SetItem()".  These functions take over ownership of the item
passed to them — even if they fail!  (Note that "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
returns.  Only when such a borrowed reference must be stored or passed
on, it must be turned into an owned reference by calling
"Py_INCREF()".

The object reference returned from a C function that is called from
Python must be an owned reference — ownership is transferred from the
function to its caller.


1.10.3. Thin Ice
----------------

There are a few situations where seemingly harmless use of a borrowed
reference can lead to problems.  These all have to do with implicit
invocations of the interpreter, which can cause the owner of a
reference to dispose of it.

The first and most important case to know about is using "Py_DECREF()"
on an unrelated object while borrowing a reference to a list item.
For instance:

   void
   bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);

       PyList_SetItem(list, 1, PyInt_FromLong(0L));
       PyObject_Print(item, stdout, 0); /* BUG! */
   }

This function first borrows a reference to "list[0]", then replaces
"list[1]" with the value "0", and finally prints the borrowed
reference. Looks harmless, right?  But it’s not!

Let’s follow the control flow into "PyList_SetItem()".  The list owns
references to all its items, so when item 1 is replaced, it has to
dispose of the original item 1.  Now let’s suppose the original item 1
was an instance of a user-defined class, and let’s further suppose
that the class defined a "__del__()" method.  If this class instance
has a reference count of 1, disposing of it will call its "__del__()"
method.

Since it is written in Python, the "__del__()" method can execute
arbitrary Python code.  Could it perhaps do something to invalidate
the reference to "item" in "bug()"?  You bet!  Assuming that the list
passed into "bug()" is accessible to the "__del__()" method, it could
execute a statement to the effect of "del list[0]", and assuming this
was the last reference to that object, it would free the memory
associated with it, thereby invalidating "item".

The solution, once you know the source of the problem, is easy:
temporarily increment the reference count.  The correct version of the
function reads:

   void
   no_bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);

       Py_INCREF(item);
       PyList_SetItem(list, 1, PyInt_FromLong(0L));
       PyObject_Print(item, stdout, 0);
       Py_DECREF(item);
   }

This is a true story.  An older version of Python contained variants
of this bug and someone spent a considerable amount of time in a C
debugger to figure out why his "__del__()" methods would fail…

The second case of problems with a borrowed reference is a variant
involving threads.  Normally, multiple threads in the Python
interpreter can’t get in each other’s way, because there is a global
lock protecting Python’s entire object space.  However, it is possible
to temporarily release this lock using the macro
"Py_BEGIN_ALLOW_THREADS", and to re-acquire it using
"Py_END_ALLOW_THREADS".  This is common around blocking I/O calls, to
let other threads use the processor while waiting for the I/O to
complete. Obviously, the following function has the same problem as
the previous one:

   void
   bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);
       Py_BEGIN_ALLOW_THREADS
       ...some blocking I/O call...
       Py_END_ALLOW_THREADS
       PyObject_Print(item, stdout, 0); /* BUG! */
   }


1.10.4. NULL Pointers
---------------------

In general, functions that take object references as arguments do not
expect you to pass them *NULL* pointers, and will dump core (or cause
later core dumps) if you do so.  Functions that return object
references generally return *NULL* only to indicate that an exception
occurred.  The reason for not testing for *NULL* arguments is that
functions often pass the objects they receive on to other function —
if each function were to test for *NULL*, there would be a lot of
redundant tests and the code would run more slowly.

It is better to test for *NULL* only at the « source: » when a pointer
that may be *NULL* is received, for example, from "malloc()" or from a
function that may raise an exception.

The macros "Py_INCREF()" and "Py_DECREF()" do not check for *NULL*
pointers — however, their variants "Py_XINCREF()" and "Py_XDECREF()"
do.

The macros for checking for a particular object type
("Pytype_Check()") don’t check for *NULL* pointers — again, there is
much code that calls several of these in a row to test an object
against various different expected types, and this would generate
redundant tests.  There are no variants with *NULL* checking.

The C function calling mechanism guarantees that the argument list
passed to C functions ("args" in the examples) is never *NULL* — in
fact it guarantees that it is always a tuple [4].

It is a severe error to ever let a *NULL* pointer « escape » to the
Python user.


1.11. Writing Extensions in C++
===============================

It is possible to write extension modules in C++.  Some restrictions
apply.  If the main program (the Python interpreter) is compiled and
linked by the C compiler, global or static objects with constructors
cannot be used.  This is not a problem if the main program is linked
by the C++ compiler.  Functions that will be called by the Python
interpreter (in particular, module initialization functions) have to
be declared using "extern "C"". It is unnecessary to enclose the
Python header files in "extern "C" {...}" — they use this form already
if the symbol "__cplusplus" is defined (all recent C++ compilers
define this symbol).


1.12. Providing a C API for an Extension Module
===============================================

Many extension modules just provide new functions and types to be used
from Python, but sometimes the code in an extension module can be
useful for other extension modules. For example, an extension module
could implement a type « collection » which works like lists without
order. Just like the standard Python list type has a C API which
permits extension modules to create and manipulate lists, this new
collection type should have a set of C functions for direct
manipulation from other extension modules.

At first sight this seems easy: just write the functions (without
declaring them "static", of course), provide an appropriate header
file, and document the C API. And in fact this would work if all
extension modules were always linked statically with the Python
interpreter. When modules are used as shared libraries, however, the
symbols defined in one module may not be visible to another module.
The details of visibility depend on the operating system; some systems
use one global namespace for the Python interpreter and all extension
modules (Windows, for example), whereas others require an explicit
list of imported symbols at module link time (AIX is one example), or
offer a choice of different strategies (most Unices). And even if
symbols are globally visible, the module whose functions one wishes to
call might not have been loaded yet!

Portability therefore requires not to make any assumptions about
symbol visibility. This means that all symbols in extension modules
should be declared "static", except for the module’s initialization
function, in order to avoid name clashes with other extension modules
(as discussed in section The Module’s Method Table and Initialization
Function). And it means that symbols that *should* be accessible from
other extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information
(pointers) from one extension module to another one: Capsules. A
Capsule is a Python data type which stores a pointer ("void *").
Capsules can only be created and accessed via their C API, but they
can be passed around like any other Python object. In particular,
they can be assigned to a name in an extension module’s namespace.
Other extension modules can then import this module, retrieve the
value of this name, and then retrieve the pointer from the Capsule.

There are many ways in which Capsules can be used to export the C API
of an extension module. Each function could get its own Capsule, or
all C API pointers could be stored in an array whose address is
published in a Capsule. And the various tasks of storing and
retrieving the pointers can be distributed in different ways between
the module providing the code and the client modules.

Whichever method you choose, it’s important to name your Capsules
properly. The function "PyCapsule_New()" takes a name parameter
("const char *"); you’re permitted to pass in a *NULL* name, but we
strongly encourage you to specify a name.  Properly named Capsules
provide a degree of runtime type-safety; there is no feasible way to
tell one unnamed Capsule from another.

In particular, Capsules used to expose C APIs should be given a name
following this convention:

   modulename.attributename

The convenience function "PyCapsule_Import()" makes it easy to load a
C API provided via a Capsule, but only if the Capsule’s name matches
this convention.  This behavior gives C API users a high degree of
certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the
burden on the writer of the exporting module, which is appropriate for
commonly used library modules. It stores all C API pointers (just one
in the example!) in an array of "void" pointers which becomes the
value of a Capsule. The header file corresponding to the module
provides a macro that takes care of importing the module and
retrieving its C API pointers; client modules only have to call this
macro before accessing the C API.

The exporting module is a modification of the "spam" module from
section Un exemple simple. The function "spam.system()" does not call
the C library function "system()" directly, but a function
"PySpam_System()", which would of course do something more complicated
in reality (such as adding « spam » to every command). This function
"PySpam_System()" is also exported to other extension modules.

The function "PySpam_System()" is a plain C function, declared
"static" like everything else:

   static int
   PySpam_System(const char *command)
   {
       return system(command);
   }

The function "spam_system()" is modified in a trivial way:

   static PyObject *
   spam_system(PyObject *self, PyObject *args)
   {
       const char *command;
       int sts;

       if (!PyArg_ParseTuple(args, "s", &command))
           return NULL;
       sts = PySpam_System(command);
       return Py_BuildValue("i", sts);
   }

In the beginning of the module, right after the line

   #include "Python.h"

two more lines must be added:

   #define SPAM_MODULE
   #include "spammodule.h"

The "#define" is used to tell the header file that it is being
included in the exporting module, not a client module. Finally, the
module’s initialization function must take care of initializing the C
API pointer array:

   PyMODINIT_FUNC
   initspam(void)
   {
       PyObject *m;
       static void *PySpam_API[PySpam_API_pointers];
       PyObject *c_api_object;

       m = Py_InitModule("spam", SpamMethods);
       if (m == NULL)
           return;

       /* Initialize the C API pointer array */
       PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

       /* Create a Capsule containing the API pointer array's address */
       c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

       if (c_api_object != NULL)
           PyModule_AddObject(m, "_C_API", c_api_object);
   }

Note that "PySpam_API" is declared "static"; otherwise the pointer
array would disappear when "initspam()" terminates!

The bulk of the work is in the header file "spammodule.h", which looks
like this:

   #ifndef Py_SPAMMODULE_H
   #define Py_SPAMMODULE_H
   #ifdef __cplusplus
   extern "C" {
   #endif

   /* Header file for spammodule */

   /* C API functions */
   #define PySpam_System_NUM 0
   #define PySpam_System_RETURN int
   #define PySpam_System_PROTO (const char *command)

   /* Total number of C API pointers */
   #define PySpam_API_pointers 1


   #ifdef SPAM_MODULE
   /* This section is used when compiling spammodule.c */

   static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

   #else
   /* This section is used in modules that use spammodule's API */

   static void **PySpam_API;

   #define PySpam_System \
    (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

   /* Return -1 on error, 0 on success.
    * PyCapsule_Import will set an exception if there's an error.
    */
   static int
   import_spam(void)
   {
       PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
       return (PySpam_API != NULL) ? 0 : -1;
   }

   #endif

   #ifdef __cplusplus
   }
   #endif

   #endif /* !defined(Py_SPAMMODULE_H) */

All that a client module must do in order to have access to the
function "PySpam_System()" is to call the function (or rather macro)
"import_spam()" in its initialization function:

   PyMODINIT_FUNC
   initclient(void)
   {
       PyObject *m;

       m = Py_InitModule("client", ClientMethods);
       if (m == NULL)
           return;
       if (import_spam() < 0)
           return;
       /* additional initialization can happen here */
   }

The main disadvantage of this approach is that the file "spammodule.h"
is rather complicated. However, the basic structure is the same for
each function that is exported, so it has to be learned only once.

Finally it should be mentioned that Capsules offer additional
functionality, which is especially useful for memory allocation and
deallocation of the pointer stored in a Capsule. The details are
described in the Python/C API Reference Manual in the section Capsules
and in the implementation of Capsules (files "Include/pycapsule.h" and
"Objects/pycapsule.c" in the Python source code distribution).

-[ Notes ]-

[1] An interface for this function already exists in the standard
    module "os" — it was chosen as a simple and straightforward
    example.

[2] The metaphor of « borrowing » a reference is not completely
    correct: the owner still has a copy of the reference.

[3] Checking that the reference count is at least 1 **does not
    work** — the reference count itself could be in freed memory and
    may thus be reused for another object!

[4] These guarantees don’t hold when you use the « old » style
    calling convention — this is still found in much existing code.
