\documentstyle[twoside,11pt,myformat]{report}

\title{Python/C API Reference}

\input{boilerplate}

\makeindex % tell \index to actually write the .idx file

\begin{document}

\pagenumbering{roman}

\maketitle

\input{copyright}

\begin{abstract}

\noindent

This manual documents the API used by C (or C++) programmers who want

to write extension modules or embed Python. It is a companion to

``Extending and Embedding the Python Interpreter'', which describes

the general principles of extension writing but does not document the

API functions in detail.

\end{abstract}

\pagebreak

{

\parskip = 0mm

\tableofcontents

}

\pagebreak

\pagenumbering{arabic}

% XXX Consider moving all this back to ext.tex and giving api.tex

% XXX a *really* short intro only.

\chapter{Introduction}

The Application Programmer's Interface to Python gives C and C++

programmers access to the Python interpreter at a variety of levels.

There are two fundamentally different reasons for using the Python/C

API. (The API is equally usable from C++, but for brevity it is

generally referred to as the Python/C API.) The first reason is to

write ``extension modules'' for specific purposes; these are C modules

that extend the Python interpreter. This is probably the most common

use. The second reason is to use Python as a component in a larger

application; this technique is generally referred to as ``embedding''

Python in an application.

Writing an extension module is a relatively well-understood process,

where a ``cookbook'' approach works well. There are several tools

that automate the process to some extent. While people have embedded

Python in other applications since its early existence, the process of

embedding Python is less straightforward that writing an extension.

Python 1.5 introduces a number of new API functions as well as some

changes to the build process that make embedding much simpler.

This manual describes the 1.5 state of affair (as of Python 1.5a3).

% XXX Eventually, take the historical notes out

Many API functions are useful independent of whether you're embedding

or extending Python; moreover, most applications that embed Python

will need to provide a custom extension as well, so it's probably a

good idea to become familiar with writing an extension before

attempting to embed Python in a real application.

\section{Objects, Types and Reference Counts}

Most Python/C API functions have one or more arguments as well as a

return value of type \code{PyObject *}. This type is a pointer

(obviously!) to an opaque data type representing an arbitrary Python

object. Since all Python object types are treated the same way by the

Python language in most situations (e.g., assignments, scope rules,

and argument passing), it is only fitting that they should be

represented by a single C type. All Python objects live on the heap:

you never declare an automatic or static variable of type

\code{PyObject}, only pointer variables of type \code{PyObject *} can

be declared.

All Python objects (even Python integers) have a ``type'' and a

``reference count''. An object's type determines what kind of object

it is (e.g., an integer, a list, or a user-defined function; there are

many more as explained in the Python Language Reference Manual). For

each of the well-known types there is a macro to check whether an

object is of that type; for instance, \code{PyList_Check(a)} is true

iff the object pointed to by \code{a} is a Python list.

\subsection{Reference Counts}

The reference count is important only because today's computers have a

finite (and often severly limited) memory size; it counts how many

different places there are that have a reference to an object. Such a

place could be another object, or a global (or static) C variable, or

a local variable in some C function. When an object's reference count

becomes zero, the object is deallocated. If it contains references to

other objects, their reference count is decremented. Those other

objects may be deallocated in turn, if this decrement makes their

reference count become zero, and so on. (There's an obvious problem

with objects that reference each other here; for now, the solution is

``don't do that''.)

Reference counts are always manipulated explicitly. The normal way is

to use the macro \code{Py_INCREF(a)} to increment an object's

reference count by one, and \code{Py_DECREF(a)} to decrement it by

one. The decref macro is considerably more complex than the incref one,

since it must check whether the reference count becomes zero and then

cause the object's deallocator, which is a function pointer contained

in the object's type structure. The type-specific deallocator takes

care of decrementing the reference counts for other objects contained

in the object, and so on, if this is a compound object type such as a

list. There's no chance that the reference count can overflow; at

least as many bits are used to hold the reference count as there are

distinct memory locations in virtual memory (assuming

\code{sizeof(long) >= sizeof(char *)}). Thus, the reference count

increment is a simple operation.

It is not necessary to increment an object's reference count for every

local variable that contains a pointer to an object. In theory, the

oject's reference count goes up by one when the variable is made to

point to it and it goes down by one when the variable goes out of

scope. However, these two cancel each other out, so at the end the

reference count hasn't changed. The only real reason to use the

reference count is to prevent the object from being deallocated as

long as our variable is pointing to it. If we know that there is at

least one other reference to the object that lives at least as long as

our variable, there is no need to increment the reference count

temporarily. An important situation where this arises is in objects

that are passed as arguments to C functions in an extension module

that are called from Python; the call mechanism guarantees to hold a

reference to every argument for the duration of the call.

However, a common pitfall is to extract an object from a list and

holding on to it for a while without incrementing its reference count.

Some other operation might conceivably remove the object from the

list, decrementing its reference count and possible deallocating it.

The real danger is that innocent-looking operations may invoke

arbitrary Python code which could do this; there is a code path which

allows control to flow back to the user from a \code{Py_DECREF()}, so

almost any operation is potentially dangerous.

A safe approach is to always use the generic operations (functions

whose name begins with \code{PyObject_}, \code{PyNumber_},

\code{PySequence_} or \code{PyMapping_}). These operations always

increment the reference count of the object they return. This leaves

the caller with the responsibility to call \code{Py_DECREF()} when

they are done with the result; this soon becomes second nature.

\subsubsection{Reference Count Details}

The reference count behavior of functions in the Python/C API is best

expelained in terms of \emph{ownership of references}. Note that we

talk of owning reference, never of owning objects; objects are always

shared! When a function owns a reference, it has to dispose of it

properly -- either by passing ownership on (usually to its caller) or

by calling \code{Py_DECREF()} or \code{Py_XDECREF()}. When a function

passes ownership of a reference on to its caller, the caller is said

to receive a \emph{new} reference. When to ownership is transferred,

the caller is said to \emph{borrow} the reference. Nothing needs to

be done for a borrowed reference.

Conversely, when calling a function while passing it a reference to an

object, there are two possibilities: the function \emph{steals} a

reference to the object, or it does not. Few functions steal

references; the two notable exceptions are \code{PyList_SetItem()} and

\code{PyTuple_SetItem()}, which steal a reference to the item (but not to

the tuple or list into which the item it put!). These functions were

designed to steal a reference because of a common idiom for

populating a tuple or list with newly created objects; e.g., the code

to create the tuple \code{(1, 2, "three")} could look like this

(forgetting about error handling for the moment):

\begin{verbatim}

PyObject *t;

t = PyTuple_New(3);

PyTuple_SetItem(t, 0, PyInt_FromLong(1L));

PyTuple_SetItem(t, 1, PyInt_FromLong(2L));

PyTuple_SetItem(t, 2, PyString_FromString("three"));

\end{verbatim}

Incidentally, \code{PyTuple_SetItem()} is the \emph{only} way to set

tuple items; \code{PyObject_SetItem()} refuses to do this since tuples

are an immutable data type. You should only use

\code{PyTuple_SetItem()} for tuples that you are creating yourself.

Equivalent code for populating a list can be written using

\code{PyList_New()} and \code{PyList_SetItem()}. Such code can also

use \code{PySequence_SetItem()}; this illustrates the difference

between the two:

\begin{verbatim}

PyObject *l, *x;

l = PyList_New(3);

x = PyInt_FromLong(1L);

PyObject_SetItem(l, 0, x); Py_DECREF(x);

x = PyInt_FromLong(2L);

PyObject_SetItem(l, 1, x); Py_DECREF(x);

x = PyString_FromString("three");

PyObject_SetItem(l, 2, x); Py_DECREF(x);

\end{verbatim}

You might find it strange that the ``recommended'' approach takes

more code. in practice, you will rarely use these ways of creating

and populating a tuple or list, however; there's a generic function,

\code{Py_BuildValue()} that can create most common objects from C

values, directed by a ``format string''. For example, the above two

blocks of code could be replaced by the following (which also takes

care of the error checking!):

\begin{verbatim}

PyObject *t, *l;

t = Py_BuildValue("(iis)", 1, 2, "three");

l = Py_BuildValue("[iis]", 1, 2, "three");

\end{verbatim}

It is much more common to use \code{PyObject_SetItem()} and friends

with items whose references you are only borrowing, like arguments

that were passed in to the function you are writing. In that case,

their behaviour regarding reference counts is much saner, since you

don't have to increment a reference count so you can give a reference

away (``have it be stolen''). For example, this function sets all

items of a list (actually, any mutable sequence) to a given item:

\begin{verbatim}

int set_all(PyObject *target, PyObject *item)

{

int i, n;

n = PyObject_Length(target);

if (n < 0)

return -1;

for (i = 0; i < n; i++) {

if (PyObject_SetItem(target, i, item) < 0)

return -1;

}

return 0;

}

\end{verbatim}

The situation is slightly different for function return values.

While passing a reference to most functions does not change your

ownership responsibilities for that reference, many functions that

return a referece to an object give you ownership of the reference.

The reason is simple: in many cases, the returned object is created

on the fly, and the reference you get is the only reference to the

object! Therefore, the generic functions that return object

references, like \code{PyObject_GetItem()} and

\code{PySequence_GetItem()}, always return a new reference (i.e., the

caller becomes the owner of the reference).

It is important to realize that whether you own a reference returned

by a function depends on which function you call only -- \emph{the

plumage} (i.e., the type of the type of the object passed as an

argument to the function) \emph{don't enter into it!} Thus, if you

extract an item from a list using \code{PyList_GetItem()}, yo don't

own the reference -- but if you obtain the same item from the same

list using \code{PySequence_GetItem()} (which happens to take exactly

the same arguments), you do own a reference to the returned object.

Here is an example of how you could write a function that computes the

sum of the items in a list of integers; once using

\code{PyList_GetItem()}, once using \code{PySequence_GetItem()}.

\begin{verbatim}

long sum_list(PyObject *list)

{

int i, n;

long total = 0;

PyObject *item;

n = PyList_Size(list);

if (n < 0)

return -1; /* Not a list */

for (i = 0; i < n; i++) {

item = PyList_GetItem(list, i); /* Can't fail */

if (!PyInt_Check(item)) continue; /* Skip non-integers */

total += PyInt_AsLong(item);

}

return total;

}

\end{verbatim}

\begin{verbatim}

long sum_sequence(PyObject *sequence)

{

int i, n;

long total = 0;

PyObject *item;

n = PyObject_Size(list);

if (n < 0)

return -1; /* Has no length */

for (i = 0; i < n; i++) {

item = PySequence_GetItem(list, i);

if (item == NULL)

return -1; /* Not a sequence, or other failure */

if (PyInt_Check(item))

total += PyInt_AsLong(item);

Py_DECREF(item); /* Discared reference ownership */

}

return total;

}

\end{verbatim}

\subsection{Types}

There are few other data types that play a significant role in

the Python/C API; most are all simple C types such as \code{int},

\code{long}, \code{double} and \code{char *}. A few structure types

are used to describe static tables used to list the functions exported

by a module or the data attributes of a new object type. These will

be discussed together with the functions that use them.

\section{Exceptions}

The Python programmer only needs to deal with exceptions if specific

error handling is required; unhandled exceptions are automatically

propagated to the caller, then to the caller's caller, and so on, till

they reach the top-level interpreter, where they are reported to the

user accompanied by a stack traceback.

For C programmers, however, error checking always has to be explicit.

All functions in the Python/C API can raise exceptions, unless an

explicit claim is made otherwise in a function's documentation. In

general, when a function encounters an error, it sets an exception,

discards any object references that it owns, and returns an

error indicator -- usually \code{NULL} or \code{-1}. A few functions

return a Boolean true/false result, with false indicating an error.

Very few functions return no explicit error indicator or have an

ambiguous return value, and require explicit testing for errors with

\code{PyErr_Occurred()}.

Exception state is maintained in per-thread storage (this is

equivalent to using global storage in an unthreaded application). A

thread can be on one of two states: an exception has occurred, or not.

The function \code{PyErr_Occurred()} can be used to check for this: it

returns a borrowed reference to the exception type object when an

exception has occurred, and \code{NULL} otherwise. There are a number

of functions to set the exception state: \code{PyErr_SetString()} is

the most common (though not the most general) function to set the

exception state, and \code{PyErr_Clear()} clears the exception state.

The full exception state consists of three objects (all of which can

be \code{NULL} ): the exception type, the corresponding exception

value, and the traceback. These have the same meanings as the Python

object \code{sys.exc_type}, \code{sys.exc_value},

\code{sys.exc_traceback}; however, they are not the same: the Python

objects represent the last exception being handled by a Python

\code{try...except} statement, while the C level exception state only

exists while an exception is being passed on between C functions until

it reaches the Python interpreter, which takes care of transferring it

to \code{sys.exc_type} and friends.

(Note that starting with Python 1.5, the preferred, thread-safe way to

access the exception state from Python code is to call the function

\code{sys.exc_info()}, which returns the per-thread exception state

for Python code. Also, the semantics of both ways to access the

exception state have changed so that a function which catches an

exception will save and restore its thread's exception state so as to

preserve the exception state of its caller. This prevents common bugs

in exception handling code caused by an innocent-looking function

overwriting the exception being handled; it also reduces the often

unwanted lifetime extension for objects that are referenced by the

stack frames in the traceback.)

As a general principle, a function that calls another function to

perform some task should check whether the called function raised an

exception, and if so, pass the exception state on to its caller. It

should discards any object references that it owns, and returns an

error indicator, but it should \emph{not} set another exception --

that would overwrite the exception that was just raised, and lose

important reason about the exact cause of the error.

A simple example of detecting exceptions and passing them on is shown

in the \code{sum_sequence()} example above. It so happens that that

example doesn't need to clean up any owned references when it detects

an error. The following example function shows some error cleanup.

First we show the equivalent Python code (to remind you why you like

Python):

\begin{verbatim}

def incr_item(seq, i):

try:

item = seq[i]

except IndexError:

item = 0

seq[i] = item + 1

\end{verbatim}

Here is the corresponding C code, in all its glory:

% XXX Is it better to have fewer comments in the code?

\begin{verbatim}

int incr_item(PyObject *seq, int i)

{

/* Objects all initialized to NULL for Py_XDECREF */

PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;

int rv = -1; /* Return value initialized to -1 (faulure) */

item = PySequence_GetItem(seq, i);

if (item == NULL) {

/* Handle IndexError only: */

if (PyErr_Occurred() != PyExc_IndexError) goto error;

/* Clear the error and use zero: */

PyErr_Clear();

item = PyInt_FromLong(1L);

if (item == NULL) goto error;

}

const_one = PyInt_FromLong(1L);

if (const_one == NULL) goto error;

incremented_item = PyNumber_Add(item, const_one);

if (incremented_item == NULL) goto error;

if (PyObject_SetItem(seq, i, incremented_item) < 0) goto error;

rv = 0; /* Success */

/* Continue with cleanup code */

error:

/* Cleanup code, shared by success and failure path */

/* Use Py_XDECREF() to ignore NULL references */

Py_XDECREF(item);

Py_XDECREF(const_one);

Py_XDECREF(incremented_item);

return rv; /* -1 for error, 0 for success */

}

\end{verbatim}

This example represents an endorsed use of the \code{goto} statement

in C! It illustrates the use of \code{PyErr_Occurred()} and

\code{PyErr_Clear()} to handle specific exceptions, and the use of

\code{Py_XDECREF()} to dispose of owned references that may be

\code{NULL} (note the `X' in the name; \code{Py_DECREF()} would crash

when confronted with a \code{NULL} reference). It is important that

the variables used to hold owned references are initialized to

\code{NULL} for this to work; likewise, the proposed return value is

initialized to \code{-1} (failure) and only set to success after

the final call made is succesful.

\section{Embedding Python}

The one important task that only embedders of the Python interpreter

have to worry about is the initialization (and possibly the

finalization) of the Python interpreter. Most functionality of the

interpreter can only be used after the interpreter has been

initialized.

The basic initialization function is \code{Py_Initialize()}. This

initializes the table of loaded modules, and creates the fundamental

modules \code{__builtin__}, \code{__main__} and \code{sys}. It also

initializes the module search path (\code{sys.path}).

\code{Py_Initialize()} does not set the ``script argument list''

(\code{sys.argv}). If this variable is needed by Python code that

will be executed later, it must be set explicitly with a call to

\code{PySys_SetArgv(\var{argc}, \var{argv})} subsequent to the call

to \code{Py_Initialize()}.

On Unix, \code{Py_Initialize()} calculates the module search path

based upon its best guess for the location of the standard Python

interpreter executable, assuming that the Python library is found in a

fixed location relative to the Python interpreter executable. In

particular, it looks for a directory named \code{lib/python1.5}

(replacing \code{1.5} with the current interpreter version) relative

to the parent directory where the executable named \code{python} is

found on the shell command search path (the environment variable

\code{\$PATH}). For instance, if the Python executable is found in

\code{/usr/local/bin/python}, it will assume that the libraries are in

\code{/usr/local/lib/python1.5}. In fact, this also the ``fallback''

location, used when no executable file named \code{python} is found

along \code{\$PATH}. The user can change this behavior by setting the

environment variable \code{\$PYTHONHOME}, and can insert additional

directories in front of the standard path by setting

\code{\$PYTHONPATH}.

The embedding application can steer the search by calling

\code{Py_SetProgramName(\var{file})} \emph{before} calling

\code{Py_Initialize()}. Note that \code{\$PYTHONHOME} still overrides

this and \code{\$PYTHONPATH} is still inserted in front of the

standard path.

Sometimes, it is desirable to ``uninitialize'' Python. For instance,

the application may want to start over (make another call to

\code{Py_Initialize()}) or the application is simply done with its

use of Python and wants to free all memory allocated by Python. This

can be accomplished by calling \code{Py_Finalize()}.

% XXX More...

\section{Embedding Python in Threaded Applications}

\chapter{Old Introduction}

(XXX This is the old introduction, mostly by Jim Fulton -- should be

rewritten.)

From the viewpoint of of C access to Python services, we have:

\begin{enumerate}

\item "Very high level layer": two or three functions that let you

exec or eval arbitrary Python code given as a string in a module whose

name is given, passing C values in and getting C values out using

mkvalue/getargs style format strings. This does not require the user

to declare any variables of type \code{PyObject *}. This should be

enough to write a simple application that gets Python code from the

user, execs it, and returns the output or errors.

\item "Abstract objects layer": which is the subject of this chapter.

It has many functions operating on objects, and lets you do many

things from C that you can also write in Python, without going through

the Python parser.

\item "Concrete objects layer": This is the public type-dependent

interface provided by the standard built-in types, such as floats,

strings, and lists. This interface exists and is currently documented

by the collection of include files provides with the Python

distributions.

\end{enumerate}

From the point of view of Python accessing services provided by C

modules:

\begin{enumerate}

\item[4.] "Python module interface": this interface consist of the basic

routines used to define modules and their members. Most of the

current extensions-writing guide deals with this interface.

\item[5.] "Built-in object interface": this is the interface that a new

built-in type must provide and the mechanisms and rules that a

developer of a new built-in type must use and follow.

\end{enumerate}

The Python C API provides four groups of operations on objects,

corresponding to the same operations in the Python language: object,

numeric, sequence, and mapping. Each protocol consists of a

collection of related operations. If an operation that is not

provided by a particular type is invoked, then the standard exception

\code{TypeError} is raised with a operation name as an argument.

In addition, for convenience this interface defines a set of

constructors for building objects of built-in types. This is needed

so new objects can be returned from C functions that otherwise treat

objects generically.

\section{Reference Counting}

For most of the functions in the Python/C API, if a function retains a

reference to a Python object passed as an argument, then the function

will increase the reference count of the object. It is unnecessary

for the caller to increase the reference count of an argument in

anticipation of the object's retention.

Usually, Python objects returned from functions should be treated as

new objects. Functions that return objects assume that the caller

will retain a reference and the reference count of the object has

already been incremented to account for this fact. A caller that does

not retain a reference to an object that is returned from a function

must decrement the reference count of the object (using

\code{Py_DECREF()}) to prevent memory leaks.

Exceptions to these rules will be noted with the individual functions.

\section{Include Files}

All function, type and macro definitions needed to use the Python/C

API are included in your code by the following line:

\code{\#include "Python.h"}

This implies inclusion of the following standard header files:

stdio.h, string.h, errno.h, and stdlib.h (if available).

All user visible names defined by Python.h (except those defined by

the included standard headers) have one of the prefixes \code{Py} or

\code{_Py}. Names beginning with \code{_Py} are for internal use

only.

\chapter{Initialization and Shutdown of an Embedded Python Interpreter}

When embedding the Python interpreter in a C or C++ program, the

interpreter must be initialized.

\begin{cfuncdesc}{void}{PyInitialize}{}

This function initializes the interpreter. It must be called before

any interaction with the interpreter takes place. If it is called

more than once, the second and further calls have no effect.

The function performs the following tasks: create an environment in

which modules can be imported and Python code can be executed;

initialize the \code{__builtin__} module; initialize the \code{sys}

module; initialize \code{sys.path}; initialize signal handling; and

create the empty \code{__main__} module.

In the current system, there is no way to undo all these

initializations or to create additional interpreter environments.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{Py_AtExit}{void (*func) ()}

Register a cleanup function to be called when Python exits. The

cleanup function will be called with no arguments and should return no

value. At most 32 cleanup functions can be registered. When the

registration is successful, \code{Py_AtExit} returns 0; on failure, it

returns -1. Each cleanup function will be called t most once. The

cleanup function registered last is called first.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_Exit}{int status}

Exit the current process. This calls \code{Py_Cleanup()} (see next

item) and performs additional cleanup (under some circumstances it

will attempt to delete all modules), and then calls the standard C

library function \code{exit(status)}.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_Cleanup}{}

Perform some of the cleanup that \code{Py_Exit} performs, but don't

exit the process. In particular, this invokes the user's

\code{sys.exitfunc} function (if defined at all), and it invokes the

cleanup functions registered with \code{Py_AtExit()}, in reverse order

of their registration.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_FatalError}{char *message}

Print a fatal error message and die. No cleanup is performed. This

function should only be invoked when a condition is detected that

would make it dangerous to continue using the Python interpreter;

e.g., when the object administration appears to be corrupted.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyImport_Init}{}

Initialize the module table. For internal use only.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyImport_Cleanup}{}

Empty the module table. For internal use only.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyBuiltin_Init}{}

Initialize the \code{__builtin__} module. For internal use only.

\end{cfuncdesc}

XXX Other init functions: PyEval_InitThreads, PyOS_InitInterrupts,

PyMarshal_Init, PySys_Init.

\chapter{Reference Counting}

The functions in this chapter are used for managing reference counts

of Python objects.

\begin{cfuncdesc}{void}{Py_INCREF}{PyObject *o}

Increment the reference count for object \code{o}. The object must

not be \NULL{}; if you aren't sure that it isn't \NULL{}, use

\code{Py_XINCREF()}.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_XINCREF}{PyObject *o}

Increment the reference count for object \code{o}. The object may be

\NULL{}, in which case the function has no effect.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_DECREF}{PyObject *o}

Decrement the reference count for object \code{o}. The object must

not be \NULL{}; if you aren't sure that it isn't \NULL{}, use

\code{Py_XDECREF()}. If the reference count reaches zero, the object's

type's deallocation function (which must not be \NULL{}) is invoked.

\strong{Warning:} The deallocation function can cause arbitrary Python

code to be invoked (e.g. when a class instance with a \code{__del__()}

method is deallocated). While exceptions in such code are not

propagated, the executed code has free access to all Python global

variables. This means that any object that is reachable from a global

variable should be in a consistent state before \code{Py_DECREF()} is

invoked. For example, code to delete an object from a list should

copy a reference to the deleted object in a temporary variable, update

the list data structure, and then call \code{Py_DECREF()} for the

temporary variable.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{Py_XDECREF}{PyObject *o}

Decrement the reference count for object \code{o}.The object may be

\NULL{}, in which case the function has no effect; otherwise the

effect is the same as for \code{Py_DECREF()}, and the same warning

applies.

\end{cfuncdesc}

The following functions are only for internal use:

\code{_Py_Dealloc}, \code{_Py_ForgetReference}, \code{_Py_NewReference},

as well as the global variable \code{_Py_RefTotal}.

\chapter{Exception Handling}

The functions in this chapter will let you handle and raise Python

exceptions. It is important to understand some of the basics of

Python exception handling. It works somewhat like the Unix

\code{errno} variable: there is a global indicator (per thread) of the

last error that occurred. Most functions don't clear this on success,

but will set it to indicate the cause of the error on failure. Most

functions also return an error indicator, usually \NULL{} if they are

supposed to return a pointer, or -1 if they return an integer

(exception: the \code{PyArg_Parse*()} functions return 1 for success and

0 for failure). When a function must fail because of some function it

called failed, it generally doesn't set the error indicator; the

function it called already set it.

The error indicator consists of three Python objects corresponding to

the Python variables \code{sys.exc_type}, \code{sys.exc_value} and

\code{sys.exc_traceback}. API functions exist to interact with the

error indicator in various ways. There is a separate error indicator

for each thread.

% XXX Order of these should be more thoughtful.

% Either alphabetical or some kind of structure.

\begin{cfuncdesc}{void}{PyErr_Print}{}

Print a standard traceback to \code{sys.stderr} and clear the error

indicator. Call this function only when the error indicator is set.

(Otherwise it will cause a fatal error!)

\end{cfuncdesc}

\begin{cfuncdesc}{PyObject *}{PyErr_Occurred}{}

Test whether the error indicator is set. If set, return the exception

\code{type} (the first argument to the last call to one of the

\code{PyErr_Set*()} functions or to \code{PyErr_Restore()}). If not

set, return \NULL{}. You do not own a reference to the return value,

so you do not need to \code{Py_DECREF()} it.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_Clear}{}

Clear the error indicator. If the error indicator is not set, there

is no effect.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_Fetch}{PyObject **ptype, PyObject **pvalue, PyObject **ptraceback}

Retrieve the error indicator into three variables whose addresses are

passed. If the error indicator is not set, set all three variables to

\NULL{}. If it is set, it will be cleared and you own a reference to

each object retrieved. The value and traceback object may be \NULL{}

even when the type object is not. \strong{Note:} this function is

normally only used by code that needs to handle exceptions or by code

that needs to save and restore the error indicator temporarily.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_Restore}{PyObject *type, PyObject *value, PyObject *traceback}

Set the error indicator from the three objects. If the error

indicator is already set, it is cleared first. If the objects are

\NULL{}, the error indicator is cleared. Do not pass a \NULL{} type

and non-\NULL{} value or traceback. The exception type should be a

string or class; if it is a class, the value should be an instance of

that class. Do not pass an invalid exception type or value.

(Violating these rules will cause subtle problems later.) This call

takes away a reference to each object, i.e. you must own a reference

to each object before the call and after the call you no longer own

these references. (If you don't understand this, don't use this

function. I warned you.) \strong{Note:} this function is normally

only used by code that needs to save and restore the error indicator

temporarily.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_SetString}{PyObject *type, char *message}

This is the most common way to set the error indicator. The first

argument specifies the exception type; it is normally one of the

standard exceptions, e.g. \code{PyExc_RuntimeError}. You need not

increment its reference count. The second argument is an error

message; it is converted to a string object.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_SetObject}{PyObject *type, PyObject *value}

This function is similar to \code{PyErr_SetString()} but lets you

specify an arbitrary Python object for the ``value'' of the exception.

You need not increment its reference count.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_SetNone}{PyObject *type}

This is a shorthand for \code{PyErr_SetString(\var{type}, Py_None}.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyErr_BadArgument}{}

This is a shorthand for \code{PyErr_SetString(PyExc_TypeError,

\var{message})}, where \var{message} indicates that a built-in operation

was invoked with an illegal argument. It is mostly for internal use.

\end{cfuncdesc}

\begin{cfuncdesc}{PyObject *}{PyErr_NoMemory}{}

This is a shorthand for \code{PyErr_SetNone(PyExc_MemoryError)}; it

returns \NULL{} so an object allocation function can write

\code{return PyErr_NoMemory();} when it runs out of memory.

\end{cfuncdesc}

\begin{cfuncdesc}{PyObject *}{PyErr_SetFromErrno}{PyObject *type}

This is a convenience function to raise an exception when a C library

function has returned an error and set the C variable \code{errno}.

It constructs a tuple object whose first item is the integer

\code{errno} value and whose second item is the corresponding error

message (gotten from \code{strerror()}), and then calls

\code{PyErr_SetObject(\var{type}, \var{object})}. On \UNIX{}, when

the \code{errno} value is \code{EINTR}, indicating an interrupted

system call, this calls \code{PyErr_CheckSignals()}, and if that set

the error indicator, leaves it set to that. The function always

returns \NULL{}, so a wrapper function around a system call can write

\code{return PyErr_NoMemory();} when the system call returns an error.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_BadInternalCall}{}

This is a shorthand for \code{PyErr_SetString(PyExc_TypeError,

\var{message})}, where \var{message} indicates that an internal

operation (e.g. a Python/C API function) was invoked with an illegal

argument. It is mostly for internal use.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyErr_CheckSignals}{}

This function interacts with Python's signal handling. It checks

whether a signal has been sent to the processes and if so, invokes the

corresponding signal handler. If the \code{signal} module is

supported, this can invoke a signal handler written in Python. In all

cases, the default effect for \code{SIGINT} is to raise the

\code{KeyboadInterrupt} exception. If an exception is raised the

error indicator is set and the function returns 1; otherwise the

function returns 0. The error indicator may or may not be cleared if

it was previously set.

\end{cfuncdesc}

\begin{cfuncdesc}{void}{PyErr_SetInterrupt}{}

This function is obsolete (XXX or platform dependent?). It simulates

the effect of a \code{SIGINT} signal arriving -- the next time

\code{PyErr_CheckSignals()} is called, \code{KeyboadInterrupt} will be

raised.

\end{cfuncdesc}

\section{Standard Exceptions}

All standard Python exceptions are available as global variables whose

names are \code{PyExc_} followed by the Python exception name.

These have the type \code{PyObject *}; they are all string objects.

For completion, here are all the variables:

\code{PyExc_AccessError},

\code{PyExc_AssertionError},

\code{PyExc_AttributeError},

\code{PyExc_EOFError},

\code{PyExc_FloatingPointError},

\code{PyExc_IOError},

\code{PyExc_ImportError},

\code{PyExc_IndexError},

\code{PyExc_KeyError},

\code{PyExc_KeyboardInterrupt},

\code{PyExc_MemoryError},

\code{PyExc_NameError},

\code{PyExc_OverflowError},

\code{PyExc_RuntimeError},

\code{PyExc_SyntaxError},

\code{PyExc_SystemError},

\code{PyExc_SystemExit},

\code{PyExc_TypeError},

\code{PyExc_ValueError},

\code{PyExc_ZeroDivisionError}.

\chapter{Utilities}

The functions in this chapter perform various utility tasks, such as

parsing function arguments and constructing Python values from C

values.

\begin{cfuncdesc}{int}{Py_FdIsInteractive}{FILE *fp, char *filename}

Return true (nonzero) if the standard I/O file \code{fp} with name

\code{filename} is deemed interactive. This is the case for files for

which \code{isatty(fileno(fp))} is true. If the global flag

\code{Py_InteractiveFlag} is true, this function also returns true if

the \code{name} pointer is \NULL{} or if the name is equal to one of

the strings \code{"<stdin>"} or \code{"???"}.

\end{cfuncdesc}

\begin{cfuncdesc}{long}{PyOS_GetLastModificationTime}{char *filename}

Return the time of last modification of the file \code{filename}.

The result is encoded in the same way as the timestamp returned by

the standard C library function \code{time()}.

\end{cfuncdesc}

\chapter{Debugging}

XXX Explain Py_DEBUG, Py_TRACE_REFS, Py_REF_DEBUG.

\chapter{The Very High Level Layer}

The functions in this chapter will let you execute Python source code

given in a file or a buffer, but they will not let you interact in a

more detailed way with the interpreter.

\begin{cfuncdesc}{int}{PyRun_AnyFile}{FILE *, char *}

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyRun_SimpleString}{char *}

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyRun_SimpleFile}{FILE *, char *}

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyRun_InteractiveOne}{FILE *, char *}

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyRun_InteractiveLoop}{FILE *, char *}

\end{cfuncdesc}

\begin{cfuncdesc}{struct _node *}{PyParser_SimpleParseString}{char *, int}

\end{cfuncdesc}

\begin{cfuncdesc}{struct _node *}{PyParser_SimpleParseFile}{FILE *, char *, int}

\end{cfuncdesc}

\begin{cfuncdesc}{}{PyObject *PyRun_String}{char *, int, PyObject *, PyObject *}

\end{cfuncdesc}

\begin{cfuncdesc}{}{PyObject *PyRun_File}{FILE *, char *, int, PyObject *, PyObject *}

\end{cfuncdesc}

\begin{cfuncdesc}{}{PyObject *Py_CompileString}{char *, char *, int}

\end{cfuncdesc}

\chapter{Abstract Objects Layer}

The functions in this chapter interact with Python objects regardless

of their type, or with wide classes of object types (e.g. all

numerical types, or all sequence types). When used on object types

for which they do not apply, they will flag a Python exception.

\section{Object Protocol}

\begin{cfuncdesc}{int}{PyObject_Print}{PyObject *o, FILE *fp, int flags}

Print an object \code{o}, on file \code{fp}. Returns -1 on error

The flags argument is used to enable certain printing

options. The only option currently supported is \code{Py_Print_RAW}.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyObject_HasAttrString}{PyObject *o, char *attr_name}

Returns 1 if o has the attribute attr_name, and 0 otherwise.

This is equivalent to the Python expression:

\code{hasattr(o,attr_name)}.

This function always succeeds.

\end{cfuncdesc}

\begin{cfuncdesc}{PyObject*}{PyObject_GetAttrString}{PyObject *o, char *attr_name}

Retrieve an attributed named attr_name from object o.

Returns the attribute value on success, or \NULL{} on failure.

This is the equivalent of the Python expression: \code{o.attr_name}.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyObject_HasAttr}{PyObject *o, PyObject *attr_name}

Returns 1 if o has the attribute attr_name, and 0 otherwise.

This is equivalent to the Python expression:

\code{hasattr(o,attr_name)}.

This function always succeeds.

\end{cfuncdesc}

\begin{cfuncdesc}{PyObject*}{PyObject_GetAttr}{PyObject *o, PyObject *attr_name}

Retrieve an attributed named attr_name form object o.

Returns the attribute value on success, or \NULL{} on failure.

This is the equivalent of the Python expression: o.attr_name.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyObject_SetAttrString}{PyObject *o, char *attr_name, PyObject *v}

Set the value of the attribute named \code{attr_name}, for object \code{o},

to the value \code{v}. Returns -1 on failure. This is

the equivalent of the Python statement: \code{o.attr_name=v}.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyObject_SetAttr}{PyObject *o, PyObject *attr_name, PyObject *v}

Set the value of the attribute named \code{attr_name}, for

object \code{o},

to the value \code{v}. Returns -1 on failure. This is

the equivalent of the Python statement: \code{o.attr_name=v}.

\end{cfuncdesc}

\begin{cfuncdesc}{int}{PyObject_DelAttrString}{PyObject *o, char *attr_name}