1. Extending Python with C or C++
*********************************

It is quite easy to add new built-in modules to Python, if you know
how to program in C.  Such *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
""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.

Note:

  The C extension interface is specific to CPython, and extension
  modules do not work on other Python implementations.  In many cases,
  it is possible to avoid writing C extensions and preserve
  portability to other implementations. For example, if your use case
  is calling C library functions or system calls, you should consider
  using the "ctypes" module or the cffi library rather than writing
  custom C code. These modules let you write Python code to interface
  with C code and are more portable between implementations of Python
  than writing and compiling a C extension module.


1.1. A Simple Example
=====================

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")

Begin by creating a file "spammodule.c".  (Historically, if a module
is called "spam", the C file containing its implementation is called
"spammodule.c"; if the module name is very long, like "spammify", the
module name can be just "spammify.c".)

The first two lines of our file can be:

   #define PY_SSIZE_T_CLEAN
   #include <Python.h>

which pulls in the Python API (you can add a comment describing the
purpose of the module and a copyright notice if you like).

Note:

  Since Python may define some pre-processor definitions which affect
  the standard headers on some systems, you *must* include "Python.h"
  before any standard headers are included.It is recommended to always
  define "PY_SSIZE_T_CLEAN" before including "Python.h".  See
  Extracting Parameters in Extension Functions for a description of
  this macro.

All user-visible symbols defined by "Python.h" have a prefix of "Py"
or "PY", except those defined in standard header files. For
convenience, and since they are used extensively by the Python
interpreter, ""Python.h"" includes a few standard header files:
"<stdio.h>", "<string.h>", "<errno.h>", and "<stdlib.h>".  If the
latter header file does not exist on your system, it declares the
functions "malloc()", "free()" and "realloc()" directly.

The next thing we add to our module file is the C function that will
be called when the Python expression "spam.system(string)" is
evaluated (we’ll see shortly how it ends up being called):

   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 PyLong_FromLong(sts);
   }

There is a straightforward translation from the argument list in
Python (for example, the single expression ""ls -l"") to the arguments
passed to the C function.  The C function always has two arguments,
conventionally named *self* and *args*.

The *self* argument points to the module object for module-level
functions; for a method it would point to the object instance.

The *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 "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.

"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).


1.2. Intermezzo: Errors and 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 result in Python of "sys.exc_info()" (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.

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

The most common one is "PyErr_SetString()".  Its arguments are an
exception object and a C string.  The exception object is usually a
predefined object like "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 "PyErr_SetFromErrno()", which only takes an
exception argument and constructs the associated value by inspection
of the global variable "errno".  The most general function is
"PyErr_SetObject()", which takes two object arguments, the exception
and its associated value.  You don’t need to "Py_INCREF()" the objects
passed to any of these functions.

You can test non-destructively whether an exception has been set with
"PyErr_Occurred()".  This returns the current exception object, or
"NULL" if no exception has occurred.  You normally don’t need to call
"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 *f* that calls another function *g* detects that the
latter fails, *f* should itself return an error value (usually "NULL"
or "-1").  It should *not* call one of the "PyErr_*()" functions — one
has already been called by *g*. *f*’s caller is then supposed to also
return an error indication to *its* caller, again *without* calling
"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 "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
"PyErr_Clear()".  The only time C code should call "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 "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, "PyLong_FromLong()") already
do this, so this note is only relevant to those who call "malloc()"
directly.

Also note that, with the important exception of "PyArg_ParseTuple()"
and friends, functions that return an integer status usually return a
positive value or zero for success and "-1" for failure, like Unix
system calls.

Finally, be careful to clean up garbage (by making "Py_XDECREF()" or
"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 "PyExc_ZeroDivisionError", which you can use directly. Of
course, you should choose exceptions wisely — don’t use
"PyExc_TypeError" to mean that a file couldn’t be opened (that should
probably be "PyExc_IOError"). If something’s wrong with the argument
list, the "PyArg_ParseTuple()" function usually raises
"PyExc_TypeError".  If you have an argument whose value must be in a
particular range or must satisfy other conditions, "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:

   static PyObject *SpamError;

and initialize it in your module’s initialization function
("PyInit_spam()") with an exception object:

   PyMODINIT_FUNC
   PyInit_spam(void)
   {
       PyObject *m;

       m = PyModule_Create(&spammodule);
       if (m == NULL)
           return NULL;

       SpamError = PyErr_NewException("spam.error", NULL, NULL);
       Py_XINCREF(SpamError);
       if (PyModule_AddObject(m, "error", SpamError) < 0) {
           Py_XDECREF(SpamError);
           Py_CLEAR(SpamError);
           Py_DECREF(m);
           return NULL;
       }

       return m;
   }

Note that the Python name for the exception object is "spam.error".
The "PyErr_NewException()" function may create a class with the base
class being "Exception" (unless another class is passed in instead of
"NULL"), described in Built-in Exceptions.

Note also that the "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 "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.

The "spam.error" exception can be raised in your extension module
using a call to "PyErr_SetString()" as shown below:

   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. Back to the Example
========================

Going back to our example function, you should now be able to
understand this statement:

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

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 "PyArg_ParseTuple()".  Otherwise the string value of
the argument has been copied to the local variable "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 "command" should
properly be declared as "const char *command").

The next statement is a call to the Unix function "system()", passing
it the string we just got from "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 "PyLong_FromLong()".

   return PyLong_FromLong(sts);

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 "void"), the corresponding Python function must return
"None".   You need this idiom to do so (which is implemented by the
"Py_RETURN_NONE" macro):

   Py_INCREF(Py_None);
   return Py_None;

"Py_None" is the C name for the special Python object "None".  It is a
genuine Python object rather than a "NULL" pointer, which means
“error” in most contexts, as we have seen.


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 referenced in the module definition
structure:

   static struct PyModuleDef spammodule = {
       PyModuleDef_HEAD_INIT,
       "spam",   /* name of module */
       spam_doc, /* module documentation, may be NULL */
       -1,       /* size of per-interpreter state of the module,
                    or -1 if the module keeps state in global variables. */
       SpamMethods
   };

This structure, in turn, must be passed to the interpreter in the
module’s initialization function.  The initialization function must be
named "PyInit_name()", where *name* is the name of the module, and
should be the only non-"static" item defined in the module file:

   PyMODINIT_FUNC
   PyInit_spam(void)
   {
       return PyModule_Create(&spammodule);
   }

Note that PyMODINIT_FUNC declares the function as "PyObject *" 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,
"PyInit_spam()" is called. (See below for comments about embedding
Python.) It calls "PyModule_Create()", which returns a module object,
and inserts built-in function objects into the newly created module
based upon the table (an array of "PyMethodDef" structures) found in
the module definition. "PyModule_Create()" returns a pointer to the
module object that it creates.  It may abort with a fatal error for
certain errors, or return "NULL" if the module could not be
initialized satisfactorily. The init function must return the module
object to its caller, so that it then gets inserted into
"sys.modules".

When embedding Python, the "PyInit_spam()" function is not called
automatically unless there’s an entry in the "PyImport_Inittab" table.
To add the module to the initialization table, use
"PyImport_AppendInittab()", optionally followed by an import of the
module:

   int
   main(int argc, char *argv[])
   {
       wchar_t *program = Py_DecodeLocale(argv[0], NULL);
       if (program == NULL) {
           fprintf(stderr, "Fatal error: cannot decode argv[0]\n");
           exit(1);
       }

       /* Add a built-in module, before Py_Initialize */
       if (PyImport_AppendInittab("spam", PyInit_spam) == -1) {
           fprintf(stderr, "Error: could not extend in-built modules table\n");
           exit(1);
       }

       /* Pass argv[0] to the Python interpreter */
       Py_SetProgramName(program);

       /* Initialize the Python interpreter.  Required.
          If this step fails, it will be a fatal error. */
       Py_Initialize();

       /* Optionally import the module; alternatively,
          import can be deferred until the embedded script
          imports it. */
       pmodule = PyImport_ImportModule("spam");
       if (!pmodule) {
           PyErr_Print();
           fprintf(stderr, "Error: could not import module 'spam'\n");
       }

       ...

       PyMem_RawFree(program);
       return 0;
   }

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.

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.

Note:

  Unlike our "spam" example, "xxmodule" uses *multi-phase
  initialization* (new in Python 3.5), where a PyModuleDef structure
  is returned from "PyInit_spam", and creation of the module is left
  to the import machinery. For details on multi-phase initialization,
  see **PEP 489**.


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 Building C and C++ Extensions) and additional information
that pertains only to building on Windows (chapter Building C and C++
Extensions 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 ("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, const 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 Parsing arguments and
building values 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!

Note that any Python object references which are provided to the
caller are *borrowed* references; do not decrement their reference
count!

Some example calls:

   #define PY_SSIZE_T_CLEAN  /* Make "s#" use Py_ssize_t rather than int. */
   #include <Python.h>

   int ok;
   int i, j;
   long k, l;
   const char *s;
   Py_ssize_t 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,
                                   const 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):

   #define PY_SSIZE_T_CLEAN  /* Make "s#" use Py_ssize_t rather than int. */
   #include <Python.h>

   static PyObject *
   keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
   {
       int voltage;
       const char *state = "a stiff";
       const char *action = "voom";
       const 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_RETURN_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)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
        "Print a lovely skit to standard output."},
       {NULL, NULL, 0, NULL}   /* sentinel */
   };

   static struct PyModuleDef keywdargmodule = {
       PyModuleDef_HEAD_INIT,
       "keywdarg",
       NULL,
       -1,
       keywdarg_methods
   };

   PyMODINIT_FUNC
   PyInit_keywdarg(void)
   {
       return PyModule_Create(&keywdargmodule);
   }


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

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

   PyObject *Py_BuildValue(const 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("y", "hello")              b'hello'
   Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
   Py_BuildValue("s#", "hello", 4)          'hell'
   Py_BuildValue("y#", "hello", 4)          b'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. The "gc" module 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).  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 "PyLong_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, "PyLong_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, PyLong_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, PyLong_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 A Simple Example. 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 PyLong_FromLong(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
   PyInit_spam(void)
   {
       PyObject *m;
       static void *PySpam_API[PySpam_API_pointers];
       PyObject *c_api_object;

       m = PyModule_Create(&spammodule);
       if (m == NULL)
           return NULL;

       /* 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 (PyModule_AddObject(m, "_C_API", c_api_object) < 0) {
           Py_XDECREF(c_api_object);
           Py_DECREF(m);
           return NULL;
       }

       return m;
   }

Note that "PySpam_API" is declared "static"; otherwise the pointer
array would disappear when "PyInit_spam()" 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
   PyInit_client(void)
   {
       PyObject *m;

       m = PyModule_Create(&clientmodule);
       if (m == NULL)
           return NULL;
       if (import_spam() < 0)
           return NULL;
       /* additional initialization can happen here */
       return m;
   }

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).

-[ Footnotes ]-

[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.
