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\title{Extending and Embedding the Python Interpreter}

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\begin{document}

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\maketitle

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\begin{abstract}

\noindent

Python is an interpreted, object-oriented programming language. This

document describes how to write modules in C or \Cpp{} to extend the

Python interpreter with new modules. Those modules can define new

functions but also new object types and their methods. The document

also describes how to embed the Python interpreter in another

application, for use as an extension language. Finally, it shows how

to compile and link extension modules so that they can be loaded

dynamically (at run time) into the interpreter, if the underlying

operating system supports this feature.

This document assumes basic knowledge about Python. For an informal

introduction to the language, see the Python Tutorial. The Python

Reference Manual gives a more formal definition of the language. The

Python Library Reference documents the existing object types,

functions and modules (both built-in and written in Python) that give

the language its wide application range.

\end{abstract}

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\parskip = 0mm

\tableofcontents

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\chapter{Extending Python with C or \Cpp{} code}

\section{Introduction}

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

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

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

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

To support extensions, the Python API (Application Programmers

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

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

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

\code{"Python.h"}.

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

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

\section{A Simple Example}

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

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

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

interface for this function already exists in the standard module

\code{os} --- it was chosen as a simple and straightfoward example.}

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

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

as follows:

\bcode\begin{verbatim}

>>> import spam

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

\end{verbatim}\ecode

%

Begin by creating a file \samp{spammodule.c}. (In general, if a

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

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

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

The first line of our file can be:

\bcode\begin{verbatim}

#include "Python.h"

\end{verbatim}\ecode

%

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

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

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

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

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

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

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

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

system, it declares the functions \code{malloc()}, \code{free()} and

\code{realloc()} directly.

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

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

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

\bcode\begin{verbatim}

static PyObject *

spam_system(self, args)

PyObject *self;

PyObject *args;

{

char *command;

int sts;

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

return NULL;

sts = system(command);

return Py_BuildValue("i", sts);

}

\end{verbatim}\ecode

%

There is a straightforward translation from the argument list in

Python (e.g.\ the single expression \code{"ls -l"}) to the arguments

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

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

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

builtin method. This will be discussed later. In the example,

\var{self} will always be a \code{NULL} pointer, since we are defining

a function, not a method. (This is done so that the interpreter

doesn't have to understand two different types of C functions.)

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

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

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

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

to convert them to C values. The function \code{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.

\code{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 by so the calling function can return

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

\section{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 \code{NULL} pointer). Exceptions

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

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

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

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

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

variables are the C equivalents of the Python variables

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

(see the section on module \code{sys} in the Library Reference

Manual). 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 \code{PyErr_SetString()}. Its arguments are an

exception object and a C string. The exception object is usually a

predefined object like \code{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 \code{PyErr_SetFromErrno()}, which only

takes an exception argument and constructs the associated value by

inspection of the (\UNIX{}) global variable \code{errno}. The most

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

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

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

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

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

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

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

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

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

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

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

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

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

to \emph{its} caller, again \emph{without} calling \code{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 \code{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 \code{PyErr_Clear()}.

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

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

completely by itself (e.g. by trying something else or pretending

nothing happened).

Note that a failing \code{malloc()} call must be turned into an

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

\code{realloc()}) must call \code{PyErr_NoMemory()} and return a

failure indicator itself. All the object-creating functions

(\code{PyInt_FromLong()} etc.) already do this, so only if you call

\code{malloc()} directly this note is of importance.

Also note that, with the important exception of

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

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

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

Finally, be careful to clean up garbage (by making \code{Py_XDECREF()}

or \code{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,

e.g. \code{PyExc_ZeroDevisionError} which you can use directly. Of

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

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

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

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

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

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

\code{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, e.g.

\bcode\begin{verbatim}

static PyObject *SpamError;

\end{verbatim}\ecode

%

and initialize it in your module's initialization function

(\code{initspam()}) with a string object, e.g. (leaving out the error

checking for now):

\bcode\begin{verbatim}

void

initspam()

{

PyObject *m, *d;

m = Py_InitModule("spam", SpamMethods);

d = PyModule_GetDict(m);

SpamError = PyString_FromString("spam.error");

PyDict_SetItemString(d, "error", SpamError);

}

\end{verbatim}\ecode

%

Note that the Python name for the exception object is

\code{spam.error}. It is conventional for module and exception names

to be spelled in lower case. It is also conventional that the

\emph{value} of the exception object is the same as its name, e.g.\

the string \code{"spam.error"}.

\section{Back to the Example}

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

understand this statement:

\bcode\begin{verbatim}

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

return NULL;

\end{verbatim}\ecode

%

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

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

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

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

\code{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

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

*command}).

The next statement is a call to the \UNIX{} function \code{system()},

passing it the string we just got from \code{PyArg_ParseTuple()}:

\bcode\begin{verbatim}

sts = system(command);

\end{verbatim}\ecode

%

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

as a Python object. This is done using the function

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

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

number of C values, and returns a new Python object. More info on

\code{Py_BuildValue()} is given later.

\bcode\begin{verbatim}

return Py_BuildValue("i", sts);

\end{verbatim}\ecode

%

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

\code{None}. You need this idiom to do so:

\bcode\begin{verbatim}

Py_INCREF(Py_None);

return Py_None;

\end{verbatim}\ecode

%

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

\code{None}. It is a genuine Python object (not a \code{NULL}

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

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

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

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

table'':

\bcode\begin{verbatim}

static PyMethodDef SpamMethods[] = {

...

{"system", spam_system, 1},

...

{NULL, NULL} /* Sentinel */

};

\end{verbatim}\ecode

%

Note the third entry (\samp{1}). This is a flag telling the

interpreter the calling convention to be used for the C function. It

should normally always be \samp{1}; a value of \samp{0} means that an

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

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

initialization function (which should be the only non-\code{static}

item defined in the module file):

\bcode\begin{verbatim}

void

initspam()

{

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

}

\end{verbatim}\ecode

%

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

\code{initspam()} is called. It calls \code{Py_InitModule()}, which

creates a ``module object'' (which is inserted in the dictionary

\code{sys.modules} under the key \code{"spam"}), and inserts built-in

function objects into the newly created module based upon the table

(an array of \code{PyMethodDef} structures) that was passed as its

second argument. \code{Py_InitModule()} returns a pointer to the

module object that it creates (which is unused here). It aborts with

a fatal error if the module could not be initialized satisfactorily,

so the caller doesn't need to check for errors.

\section{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 depend on the style of dynamic loading your

system uses; see the chapter on Dynamic Loading for more info 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: just place your file (\file{spammodule.c} for example) in

the \file{Modules} directory, add a line to the file

\file{Modules/Setup} describing your file:

\bcode\begin{verbatim}

spam spammodule.o

\end{verbatim}\ecode

%

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

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

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

there by running \code{make Makefile}. (This is necessary each time

you change the \file{Setup} file.)

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

be listed on the line in the \file{Setup} file as well, for instance:

\bcode\begin{verbatim}

spam spammodule.o -lX11

\end{verbatim}\ecode

%

\section{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 \samp{-c} command line option in \file{Python/pythonmain.c}.)

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

\code{Py_INCREF()} it!) in a global variable --- or whereever you see fit.

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

definition:

\bcode\begin{verbatim}

static PyObject *my_callback = NULL;

static PyObject *

my_set_callback(dummy, arg)

PyObject *dummy, *arg;

{

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

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

my_callback = arg; /* Remember new callback */

/* Boilerplate to return "None" */

Py_INCREF(Py_None);

return Py_None;

}

\end{verbatim}\ecode

%

The macros \code{Py_XINCREF()} and \code{Py_XDECREF()} increment/decrement

the reference count of an object and are safe in the presence of

\code{NULL} pointers. More info on them in the section on Reference

Counts below.

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

\code{PyEval_CallObject()}. This function has two arguments, both

pointers to arbitrary Python objects: the Python function, and the

argument list. The argument list must always be a tuple object, whose

length is the number of arguments. To call the Python function with

no arguments, pass an empty tuple; to call it with one argument, pass

a singleton tuple. \code{Py_BuildValue()} returns a tuple when its

format string consists of zero or more format codes between

parentheses. For example:

\bcode\begin{verbatim}

int arg;

PyObject *arglist;

PyObject *result;

...

arg = 123;

...

/* Time to call the callback */

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

result = PyEval_CallObject(my_callback, arglist);

Py_DECREF(arglist);

\end{verbatim}\ecode

%

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

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

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

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

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

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

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

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

global variable, you should somehow \code{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 \code{NULL}. If it is, the Python function terminated by raising

an exception. If the C code that called \code{PyEval_CallObject()} is

called from Python, it should now return an error indication to its

Python caller, so the interpreter can print a stack trace, or the

calling Python code can handle the exception. If this is not possible

or desirable, the exception should be cleared by calling

\code{PyErr_Clear()}. For example:

\bcode\begin{verbatim}

if (result == NULL)

return NULL; /* Pass error back */

...use result...

Py_DECREF(result);

\end{verbatim}\ecode

%

Depending on the desired interface to the Python callback function,

you may also have to provide an argument list to \code{PyEval_CallObject()}.

In some cases the argument list is also provided by the Python

program, through the same interface that specified the callback

function. It can then be saved and used in the same manner as the

function object. In other cases, you may have to construct a new

tuple to pass as the argument list. The simplest way to do this is to

call \code{Py_BuildValue()}. For example, if you want to pass an integral

event code, you might use the following code:

\bcode\begin{verbatim}

PyObject *arglist;

...

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

result = PyEval_CallObject(my_callback, arglist);

Py_DECREF(arglist);

if (result == NULL)

return NULL; /* Pass error back */

/* Here maybe use the result */

Py_DECREF(result);

\end{verbatim}\ecode

%

Note the placement of \code{Py_DECREF(argument)} immediately after the call,

before the error check! Also note that strictly spoken this code is

not complete: \code{Py_BuildValue()} may run out of memory, and this should

be checked.

\section{Format Strings for {\tt PyArg_ParseTuple()}}

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

\bcode\begin{verbatim}

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

\end{verbatim}\ecode

%

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

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

must be a format string, whose syntax is explained below. The

remaining arguments must be addresses of variables whose type is

determined by the format string. For the conversion to succeed, the

\var{arg} object must match the format and the format must be

exhausted.

Note that while \code{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!

A format string consists of zero or more ``format units''. A format

unit describes one Python object; it is usually a single character or

a parenthesized sequence of format units. With a few exceptions, a

format unit that is not a parenthesized sequence normally corresponds

to a single address argument to \code{PyArg_ParseTuple()}. In the

following description, the quoted form is the format unit; the entry

in (round) parentheses is the Python object type that matches the

format unit; and the entry in [square] brackets is the type of the C

variable(s) whose address should be passed. (Use the \samp{\&}

operator to pass a variable's address.)

\begin{description}

\item[\samp{s} (string) [char *]]

Convert a Python string to a C pointer to a character string. You

must not provide storage for the string itself; a pointer to an

existing string is stored into the character pointer variable whose

address you pass. The C string is null-terminated. The Python string

must not contain embedded null bytes; if it does, a \code{TypeError}

exception is raised.

\item[\samp{s\#} (string) {[char *, int]}]

This variant on \code{'s'} stores into two C variables, the first one

a pointer to a character string, the second one its length. In this

case the Python string may contain embedded null bytes.

\item[\samp{z} (string or \code{None}) {[char *]}]

Like \samp{s}, but the Python object may also be \code{None}, in which

case the C pointer is set to \code{NULL}.

\item[\samp{z\#} (string or \code{None}) {[char *, int]}]

This is to \code{'s\#'} as \code{'z'} is to \code{'s'}.

\item[\samp{b} (integer) {[char]}]

Convert a Python integer to a tiny int, stored in a C \code{char}.

\item[\samp{h} (integer) {[short int]}]

Convert a Python integer to a C \code{short int}.

\item[\samp{i} (integer) {[int]}]

Convert a Python integer to a plain C \code{int}.

\item[\samp{l} (integer) {[long int]}]

Convert a Python integer to a C \code{long int}.

\item[\samp{c} (string of length 1) {[char]}]

Convert a Python character, represented as a string of length 1, to a

C \code{char}.

\item[\samp{f} (float) {[float]}]

Convert a Python floating point number to a C \code{float}.

\item[\samp{d} (float) {[double]}]

Convert a Python floating point number to a C \code{double}.

\item[\samp{O} (object) {[PyObject *]}]

Store a Python object (without any conversion) in a C object pointer.

The C program thus receives the actual object that was passed. The

object's reference count is not increased. The pointer stored is not

\code{NULL}.

\item[\samp{O!} (object) {[\var{typeobject}, PyObject *]}]

Store a Python object in a C object pointer. This is similar to

\samp{O}, but takes two C arguments: the first is the address of a

Python type object, the second is the address of the C variable (of

type \code{PyObject *}) into which the object pointer is stored.

If the Python object does not have the required type, a

\code{TypeError} exception is raised.

\item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}]

Convert a Python object to a C variable through a \var{converter}

function. This takes two arguments: the first is a function, the

second is the address of a C variable (of arbitrary type), converted

to \code{void *}. The \var{converter} function in turn is called as

follows:

\code{\var{status} = \var{converter}(\var{object}, \var{address});}

where \var{object} is the Python object to be converted and

\var{address} is the \code{void *} argument that was passed to

\code{PyArg_ConvertTuple()}. The returned \var{status} should be

\code{1} for a successful conversion and \code{0} if the conversion

has failed. When the conversion fails, the \var{converter} function

should raise an exception.

\item[\samp{S} (string) {[PyStringObject *]}]

Like \samp{O} but raises a \code{TypeError} exception that the object

is a string object. The C variable may also be declared as

\code{PyObject *}.

\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}]

The object must be a Python tuple whose length is the number of format

units in \var{items}. The C arguments must correspond to the

individual format units in \var{items}. Format units for tuples may

be nested.

\end{description}

It is possible to pass Python long integers where integers are

requested; however no proper range checking is done -- the most

significant bits are silently truncated when the receiving field is

too small to receive the value (actually, the semantics are inherited

from downcasts in C --- your milage may vary).

A few other characters have a meaning in a format string. These may

not occur inside nested parentheses. They are:

\begin{description}

\item[\samp{|}]

Indicates that the remaining arguments in the Python argument list are

optional. The C variables corresponding to optional arguments should

be initialized to their default value --- when an optional argument is

not specified, the \code{PyArg_ParseTuple} does not touch the contents

of the corresponding C variable(s).

\item[\samp{:}]

The list of format units ends here; the string after the colon is used

as the function name in error messages (the ``associated value'' of

the exceptions that \code{PyArg_ParseTuple} raises).

\item[\samp{;}]

The list of format units ends here; the string after the colon is used

as the error message \emph{instead} of the default error message.

Clearly, \samp{:} and \samp{;} mutually exclude each other.

\end{description}

Some example calls:

\bcode\begin{verbatim}

int ok;

int i, j;

long k, l;

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') */

{

char *file;

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)) */

}

\end{verbatim}\ecode

%

\section{The {\tt Py_BuildValue()} Function}

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

declared as follows:

\bcode\begin{verbatim}

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

\end{verbatim}\ecode

%

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

\code{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 \code{PyArg_ParseTuple()}: while the latter

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

are always represented as tuples internally), \code{BuildValue()} does

not always build a tuple. It builds a tuple only if its format string

contains two or more format units. If the format string is empty, it

returns \code{None}; if it contains exactly one format unit, it

returns whatever object is described by that format unit. To force it

to return a tuple of size 0 or one, parenthesize the format string.

In the following description, the quoted form is the format unit; the

entry in (round) parentheses is the Python object type that the format

unit will return; and the entry in [square] brackets is the type of

the C value(s) to be passed.

The characters space, tab, colon and comma are ignored in format

strings (but not within format units such as \samp{s\#}). This can be

used to make long format strings a tad more readable.

\begin{description}

\item[\samp{s} (string) {[char *]}]

Convert a null-terminated C string to a Python object. If the C

string pointer is \code{NULL}, \code{None} is returned.

\item[\samp{s\#} (string) {[char *, int]}]

Convert a C string and its length to a Python object. If the C string

pointer is \code{NULL}, the length is ignored and \code{None} is

returned.

\item[\samp{z} (string or \code{None}) {[char *]}]

Same as \samp{s}.

\item[\samp{z\#} (string or \code{None}) {[char *, int]}]

Same as \samp{s\#}.

\item[\samp{i} (integer) {[int]}]

Convert a plain C \code{int} to a Python integer object.

\item[\samp{b} (integer) {[char]}]

Same as \samp{i}.

\item[\samp{h} (integer) {[short int]}]

Same as \samp{i}.

\item[\samp{l} (integer) {[long int]}]

Convert a C \code{long int} to a Python integer object.

\item[\samp{c} (string of length 1) {[char]}]

Convert a C \code{int} representing a character to a Python string of

length 1.

\item[\samp{d} (float) {[double]}]

Convert a C \code{double} to a Python floating point number.

\item[\samp{f} (float) {[float]}]

Same as \samp{d}.

\item[\samp{O} (object) {[PyObject *]}]

Pass a Python object untouched (except for its reference count, which

is incremented by one). If the object passed in is a \code{NULL}

pointer, it is assumed that this was caused because the call producing

the argument found an error and set an exception. Therefore,

\code{Py_BuildValue()} will return \code{NULL} but won't raise an

exception. If no exception has been raised yet,

\code{PyExc_SystemError} is set.

\item[\samp{S} (object) {[PyObject *]}]

Same as \samp{O}.

\item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}]

Convert \var{anything} to a Python object through a \var{converter}

function. The function is called with \var{anything} (which should be

compatible with \code{void *}) as its argument and should return a

``new'' Python object, or \code{NULL} if an error occurred.

\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}]

Convert a sequence of C values to a Python tuple with the same number

of items.

\item[\samp{[\var{items}]} (list) {[\var{matching-items}]}]

Convert a sequence of C values to a Python list with the same number

of items.

\item[\samp{\{\var{items}\}} (dictionary) {[\var{matching-items}]}]

Convert a sequence of C values to a Python dictionary. Each pair of

consecutive C values adds one item to the dictionary, serving as key

and value, respectively.

\end{description}

If there is an error in the format string, the

\code{PyExc_SystemError} exception is raised and \code{NULL} returned.

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

\bcode\begin{verbatim}

Py_BuildValue("") None

Py_BuildValue("i", 123) 123

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

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

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

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

Py_BuildValue("()") ()

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

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

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

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

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

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

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

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

\end{verbatim}\ecode

%

\section{Reference Counts}

\subsection{Introduction}

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

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

is done using the functions \code{malloc()} and \code{free()}. In

\Cpp{}, the operators \code{new} and \code{delete} are used with

essentially the same meaning; they are actually implemented using

\code{malloc()} and \code{free()}, so we'll restrict the following

discussion to the latter.

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

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

\code{free()}. It is important to call \code{free()} at the right

time. If a block's address is forgotten but \code{free()} is not

called for it, the memory it occupies cannot be reused until the

program terminates. This is called a \dfn{memory leak}. On the other

hand, if a program calls \code{free()} for a block and then continues

to use the block, it creates a conflict with re-use of the block

through another \code{malloc()} call. This is called \dfn{using freed

memory}. It has the same bad consequences as referencing uninitialized

data --- core dumps, wrong results, mysterious crashes.

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

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

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

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

detects an error condition and can return prematurely from the

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

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

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

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

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

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

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

happening by having a coding convention or strategy that minimizes

this kind of errors.

Since Python makes heavy use of \code{malloc()} and \code{free()}, it

needs a strategy to avoid memory leaks as well as the use of freed

memory. The chosen method is called \dfn{reference counting}. The

principle is simple: every object contains a counter, which is

incremented when a reference to the object is stored somewhere, and

which is decremented when a reference to it is deleted. When the

counter reaches zero, the last reference to the object has been

deleted and the object is freed.

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

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

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

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

user doesn't need to call \code{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 \code{malloc()}

and \code{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.

\subsection{Reference Counting in Python}

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

which handle the incrementing and decrementing of the reference count.

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

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

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

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

pointer to its type object.

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

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

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

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

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

calling \code{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

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

a memory leak.

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

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

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

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

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

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

it risks using freed memory and should be avoided

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

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

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

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

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

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

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

disadvantage of borrowing over leaking is that there are some subtle

situations where in seemingly correct code a borrowed reference can be

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

of it.

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

\code{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 (i.e., the new owner must

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

\subsection{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, e.g.\ \code{PyInt_FromLong()} and

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

fact, in some cases, you don't receive a reference to a brand new

object, you still receive ownership of the reference. For instance,

\code{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

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

however, since a few common routines are exceptions:

\code{PyTuple_GetItem()}, \code{PyList_GetItem()} and

\code{PyDict_GetItem()} (and \code{PyDict_GetItemString()}) all return

references that you borrow from the tuple, list or dictionary.

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

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

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

\code{sys.modules}.

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

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

it, it will use \code{Py_INCREF()} to become an independent owner.

There are exactly two important exceptions to this rule:

\code{PyTuple_SetItem()} and \code{PyList_SetItem()}. These functions

take over ownership of the item passed to them --- even if they fail!