2594 lines
98 KiB
TeX
2594 lines
98 KiB
TeX
\documentclass{manual}
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% XXX PM explain how to add new types to Python
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\title{Extending and Embedding the Python Interpreter}
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\input{boilerplate}
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% Tell \index to actually write the .idx file
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\makeindex
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\begin{document}
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\maketitle
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\ifhtml
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\chapter*{Front Matter\label{front}}
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\fi
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\input{copyright}
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\begin{abstract}
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\noindent
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Python is an interpreted, object-oriented programming language. This
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document describes how to write modules in C or \Cpp{} to extend the
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Python interpreter with new modules. Those modules can define new
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functions but also new object types and their methods. The document
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also describes how to embed the Python interpreter in another
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application, for use as an extension language. Finally, it shows how
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to compile and link extension modules so that they can be loaded
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dynamically (at run time) into the interpreter, if the underlying
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operating system supports this feature.
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This document assumes basic knowledge about Python. For an informal
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introduction to the language, see the
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\citetitle[../tut/tut.html]{Python Tutorial}. The
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\citetitle[../ref/ref.html]{Python Reference Manual} gives a more
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formal definition of the language. The
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\citetitle[../lib/lib.html]{Python Library Reference} documents the
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existing object types, functions and modules (both built-in and
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written in Python) that give the language its wide application range.
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For a detailed description of the whole Python/C API, see the separate
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\citetitle[../api/api.html]{Python/C API Reference Manual}.
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\end{abstract}
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\tableofcontents
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\chapter{Extending Python with C or \Cpp{} \label{intro}}
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It is quite easy to add new built-in modules to Python, if you know
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how to program in C. Such \dfn{extension modules} can do two things
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that can't be done directly in Python: they can implement new built-in
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object types, and they can call C library functions and system calls.
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To support extensions, the Python API (Application Programmers
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Interface) defines a set of functions, macros and variables that
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provide access to most aspects of the Python run-time system. The
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Python API is incorporated in a C source file by including the header
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\code{"Python.h"}.
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The compilation of an extension module depends on its intended use as
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well as on your system setup; details are given in later chapters.
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\section{A Simple Example
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\label{simpleExample}}
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Let's create an extension module called \samp{spam} (the favorite food
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of Monty Python fans...) and let's say we want to create a Python
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interface to the C library function \cfunction{system()}.\footnote{An
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interface for this function already exists in the standard module
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\module{os} --- it was chosen as a simple and straightfoward example.}
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This function takes a null-terminated character string as argument and
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returns an integer. We want this function to be callable from Python
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as follows:
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\begin{verbatim}
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>>> import spam
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>>> status = spam.system("ls -l")
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\end{verbatim}
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Begin by creating a file \file{spammodule.c}. (Historically, if a
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module is called \samp{spam}, the C file containing its implementation
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is called \file{spammodule.c}; if the module name is very long, like
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\samp{spammify}, the module name can be just \file{spammify.c}.)
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The first line of our file can be:
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\begin{verbatim}
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#include <Python.h>
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\end{verbatim}
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which pulls in the Python API (you can add a comment describing the
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purpose of the module and a copyright notice if you like).
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All user-visible symbols defined by \code{"Python.h"} have a prefix of
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\samp{Py} or \samp{PY}, except those defined in standard header files.
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For convenience, and since they are used extensively by the Python
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interpreter, \code{"Python.h"} includes a few standard header files:
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\code{<stdio.h>}, \code{<string.h>}, \code{<errno.h>}, and
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\code{<stdlib.h>}. If the latter header file does not exist on your
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system, it declares the functions \cfunction{malloc()},
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\cfunction{free()} and \cfunction{realloc()} directly.
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The next thing we add to our module file is the C function that will
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be called when the Python expression \samp{spam.system(\var{string})}
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is evaluated (we'll see shortly how it ends up being called):
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\begin{verbatim}
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static PyObject *
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spam_system(self, args)
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PyObject *self;
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PyObject *args;
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{
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char *command;
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int sts;
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if (!PyArg_ParseTuple(args, "s", &command))
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return NULL;
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sts = system(command);
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return Py_BuildValue("i", sts);
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}
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\end{verbatim}
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There is a straightforward translation from the argument list in
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Python (e.g.\ the single expression \code{"ls -l"}) to the arguments
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passed to the C function. The C function always has two arguments,
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conventionally named \var{self} and \var{args}.
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The \var{self} argument is only used when the C function implements a
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built-in method, not a function. In the example, \var{self} will
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always be a \NULL{} pointer, since we are defining a function, not a
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method. (This is done so that the interpreter doesn't have to
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understand two different types of C functions.)
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The \var{args} argument will be a pointer to a Python tuple object
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containing the arguments. Each item of the tuple corresponds to an
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argument in the call's argument list. The arguments are Python
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objects --- in order to do anything with them in our C function we have
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to convert them to C values. The function \cfunction{PyArg_ParseTuple()}
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in the Python API checks the argument types and converts them to C
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values. It uses a template string to determine the required types of
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the arguments as well as the types of the C variables into which to
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store the converted values. More about this later.
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\cfunction{PyArg_ParseTuple()} returns true (nonzero) if all arguments have
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the right type and its components have been stored in the variables
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whose addresses are passed. It returns false (zero) if an invalid
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argument list was passed. In the latter case it also raises an
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appropriate exception so the calling function can return
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\NULL{} immediately (as we saw in the example).
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\section{Intermezzo: Errors and Exceptions
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\label{errors}}
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An important convention throughout the Python interpreter is the
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following: when a function fails, it should set an exception condition
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and return an error value (usually a \NULL{} pointer). Exceptions
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are stored in a static global variable inside the interpreter; if this
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variable is \NULL{} no exception has occurred. A second global
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variable stores the ``associated value'' of the exception (the second
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argument to \keyword{raise}). A third variable contains the stack
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traceback in case the error originated in Python code. These three
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variables are the C equivalents of the Python variables
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\code{sys.exc_type}, \code{sys.exc_value} and \code{sys.exc_traceback} (see
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the section on module \module{sys} in the
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\citetitle[../lib/lib.html]{Python Library Reference}). It is
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important to know about them to understand how errors are passed
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around.
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The Python API defines a number of functions to set various types of
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exceptions.
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The most common one is \cfunction{PyErr_SetString()}. Its arguments
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are an exception object and a C string. The exception object is
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usually a predefined object like \cdata{PyExc_ZeroDivisionError}. The
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C string indicates the cause of the error and is converted to a
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Python string object and stored as the ``associated value'' of the
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exception.
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Another useful function is \cfunction{PyErr_SetFromErrno()}, which only
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takes an exception argument and constructs the associated value by
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inspection of the global variable \cdata{errno}. The most
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general function is \cfunction{PyErr_SetObject()}, which takes two object
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arguments, the exception and its associated value. You don't need to
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\cfunction{Py_INCREF()} the objects passed to any of these functions.
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You can test non-destructively whether an exception has been set with
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\cfunction{PyErr_Occurred()}. This returns the current exception object,
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or \NULL{} if no exception has occurred. You normally don't need
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to call \cfunction{PyErr_Occurred()} to see whether an error occurred in a
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function call, since you should be able to tell from the return value.
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When a function \var{f} that calls another function \var{g} detects
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that the latter fails, \var{f} should itself return an error value
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(e.g.\ \NULL{} or \code{-1}). It should \emph{not} call one of the
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\cfunction{PyErr_*()} functions --- one has already been called by \var{g}.
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\var{f}'s caller is then supposed to also return an error indication
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to \emph{its} caller, again \emph{without} calling \cfunction{PyErr_*()},
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and so on --- the most detailed cause of the error was already
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reported by the function that first detected it. Once the error
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reaches the Python interpreter's main loop, this aborts the currently
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executing Python code and tries to find an exception handler specified
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by the Python programmer.
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(There are situations where a module can actually give a more detailed
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error message by calling another \cfunction{PyErr_*()} function, and in
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such cases it is fine to do so. As a general rule, however, this is
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not necessary, and can cause information about the cause of the error
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to be lost: most operations can fail for a variety of reasons.)
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To ignore an exception set by a function call that failed, the exception
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condition must be cleared explicitly by calling \cfunction{PyErr_Clear()}.
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The only time C code should call \cfunction{PyErr_Clear()} is if it doesn't
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want to pass the error on to the interpreter but wants to handle it
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completely by itself (e.g.\ by trying something else or pretending
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nothing happened).
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Every failing \cfunction{malloc()} call must be turned into an
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exception --- the direct caller of \cfunction{malloc()} (or
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\cfunction{realloc()}) must call \cfunction{PyErr_NoMemory()} and
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return a failure indicator itself. All the object-creating functions
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(for example, \cfunction{PyInt_FromLong()}) already do this, so this
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note is only relevant to those who call \cfunction{malloc()} directly.
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Also note that, with the important exception of
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\cfunction{PyArg_ParseTuple()} and friends, functions that return an
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integer status usually return a positive value or zero for success and
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\code{-1} for failure, like \UNIX{} system calls.
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Finally, be careful to clean up garbage (by making
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\cfunction{Py_XDECREF()} or \cfunction{Py_DECREF()} calls for objects
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you have already created) when you return an error indicator!
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The choice of which exception to raise is entirely yours. There are
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predeclared C objects corresponding to all built-in Python exceptions,
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e.g.\ \cdata{PyExc_ZeroDivisionError}, which you can use directly. Of
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course, you should choose exceptions wisely --- don't use
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\cdata{PyExc_TypeError} to mean that a file couldn't be opened (that
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should probably be \cdata{PyExc_IOError}). If something's wrong with
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the argument list, the \cfunction{PyArg_ParseTuple()} function usually
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raises \cdata{PyExc_TypeError}. If you have an argument whose value
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must be in a particular range or must satisfy other conditions,
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\cdata{PyExc_ValueError} is appropriate.
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You can also define a new exception that is unique to your module.
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For this, you usually declare a static object variable at the
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beginning of your file, e.g.
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\begin{verbatim}
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static PyObject *SpamError;
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\end{verbatim}
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and initialize it in your module's initialization function
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(\cfunction{initspam()}) with an exception object, e.g.\ (leaving out
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the error checking for now):
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\begin{verbatim}
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void
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initspam()
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{
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PyObject *m, *d;
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m = Py_InitModule("spam", SpamMethods);
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d = PyModule_GetDict(m);
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SpamError = PyErr_NewException("spam.error", NULL, NULL);
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PyDict_SetItemString(d, "error", SpamError);
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}
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\end{verbatim}
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Note that the Python name for the exception object is
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\exception{spam.error}. The \cfunction{PyErr_NewException()} function
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may create a class with the base class being \exception{Exception}
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(unless another class is passed in instead of \NULL), described in the
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\citetitle[../lib/lib.html]{Python Library Reference} under ``Built-in
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Exceptions.''
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Note also that the \cdata{SpamError} variable retains a reference to
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the newly created exception class; this is intentional! Since the
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exception could be removed from the module by external code, an owned
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reference to the class is needed to ensure that it will not be
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discarded, causing \cdata{SpamError} to become a dangling pointer.
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Should it become a dangling pointer, C code which raises the exception
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could cause a core dump or other unintended side effects.
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\section{Back to the Example
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\label{backToExample}}
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Going back to our example function, you should now be able to
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understand this statement:
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\begin{verbatim}
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if (!PyArg_ParseTuple(args, "s", &command))
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return NULL;
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\end{verbatim}
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It returns \NULL{} (the error indicator for functions returning
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object pointers) if an error is detected in the argument list, relying
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on the exception set by \cfunction{PyArg_ParseTuple()}. Otherwise the
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string value of the argument has been copied to the local variable
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\cdata{command}. This is a pointer assignment and you are not supposed
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to modify the string to which it points (so in Standard C, the variable
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\cdata{command} should properly be declared as \samp{const char
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*command}).
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The next statement is a call to the \UNIX{} function
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\cfunction{system()}, passing it the string we just got from
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\cfunction{PyArg_ParseTuple()}:
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\begin{verbatim}
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sts = system(command);
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\end{verbatim}
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Our \function{spam.system()} function must return the value of
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\cdata{sts} as a Python object. This is done using the function
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\cfunction{Py_BuildValue()}, which is something like the inverse of
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\cfunction{PyArg_ParseTuple()}: it takes a format string and an
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arbitrary number of C values, and returns a new Python object.
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More info on \cfunction{Py_BuildValue()} is given later.
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\begin{verbatim}
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return Py_BuildValue("i", sts);
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\end{verbatim}
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In this case, it will return an integer object. (Yes, even integers
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are objects on the heap in Python!)
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If you have a C function that returns no useful argument (a function
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returning \ctype{void}), the corresponding Python function must return
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\code{None}. You need this idiom to do so:
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\begin{verbatim}
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Py_INCREF(Py_None);
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return Py_None;
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\end{verbatim}
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\cdata{Py_None} is the C name for the special Python object
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\code{None}. It is a genuine Python object rather than a \NULL{}
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pointer, which means ``error'' in most contexts, as we have seen.
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\section{The Module's Method Table and Initialization Function
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\label{methodTable}}
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I promised to show how \cfunction{spam_system()} is called from Python
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programs. First, we need to list its name and address in a ``method
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table'':
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\begin{verbatim}
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static PyMethodDef SpamMethods[] = {
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...
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{"system", spam_system, METH_VARARGS},
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...
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{NULL, NULL} /* Sentinel */
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};
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\end{verbatim}
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Note the third entry (\samp{METH_VARARGS}). This is a flag telling
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the interpreter the calling convention to be used for the C
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function. It should normally always be \samp{METH_VARARGS} or
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\samp{METH_VARARGS | METH_KEYWORDS}; a value of \code{0} means that an
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obsolete variant of \cfunction{PyArg_ParseTuple()} is used.
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When using only \samp{METH_VARARGS}, the function should expect
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the Python-level parameters to be passed in as a tuple acceptable for
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parsing via \cfunction{PyArg_ParseTuple()}; more information on this
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function is provided below.
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The \constant{METH_KEYWORDS} bit may be set in the third field if
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keyword arguments should be passed to the function. In this case, the
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C function should accept a third \samp{PyObject *} parameter which
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will be a dictionary of keywords. Use
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\cfunction{PyArg_ParseTupleAndKeywords()} to parse the arguments to
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such a function.
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The method table must be passed to the interpreter in the module's
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initialization function. The initialization function must be named
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\cfunction{init\var{name}()}, where \var{name} is the name of the
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module, and should be the only non-\keyword{static} item defined in
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the module file:
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\begin{verbatim}
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void
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initspam()
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{
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(void) Py_InitModule("spam", SpamMethods);
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}
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\end{verbatim}
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Note that for \Cpp, this method must be declared \code{extern "C"}.
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When the Python program imports module \module{spam} for the first
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time, \cfunction{initspam()} is called. (See below for comments about
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embedding Python.) It calls
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\cfunction{Py_InitModule()}, which creates a ``module object'' (which
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is inserted in the dictionary \code{sys.modules} under the key
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\code{"spam"}), and inserts built-in function objects into the newly
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created module based upon the table (an array of \ctype{PyMethodDef}
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structures) that was passed as its second argument.
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\cfunction{Py_InitModule()} returns a pointer to the module object
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that it creates (which is unused here). It aborts with a fatal error
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if the module could not be initialized satisfactorily, so the caller
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doesn't need to check for errors.
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When embedding Python, the \cfunction{initspam()} function is not
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called automatically unless there's an entry in the
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\cdata{_PyImport_Inittab} table. The easiest way to handle this is to
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statically initialize your statically-linked modules by directly
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calling \cfunction{initspam()} after the call to
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\cfunction{Py_Initialize()} or \cfunction{PyMac_Initialize()}:
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\begin{verbatim}
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int main(int argc, char **argv)
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{
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/* Pass argv[0] to the Python interpreter */
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Py_SetProgramName(argv[0]);
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/* Initialize the Python interpreter. Required. */
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Py_Initialize();
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/* Add a static module */
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initspam();
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\end{verbatim}
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An example may be found in the file \file{Demo/embed/demo.c} in the
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Python source distribution.
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\strong{Note:} Removing entries from \code{sys.modules} or importing
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compiled modules into multiple interpreters within a process (or
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following a \cfunction{fork()} without an intervening
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\cfunction{exec()}) can create problems for some extension modules.
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Extension module authors should exercise caution when initializing
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internal data structures.
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Note also that the \function{reload()} function can be used with
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extension modules, and will call the module initialization function
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(\cfunction{initspam()} in the example), but will not load the module
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again if it was loaded from a dynamically loadable object file
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(\file{.so} on \UNIX, \file{.dll} on Windows).
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A more substantial example module is included in the Python source
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distribution as \file{Modules/xxmodule.c}. This file may be used as a
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template or simply read as an example. The \program{modulator.py}
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script included in the source distribution or Windows install provides
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a simple graphical user interface for declaring the functions and
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objects which a module should implement, and can generate a template
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which can be filled in. The script lives in the
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\file{Tools/modulator/} directory; see the \file{README} file there
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for more information.
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\section{Compilation and Linkage
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\label{compilation}}
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There are two more things to do before you can use your new extension:
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compiling and linking it with the Python system. If you use dynamic
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loading, the details depend on the style of dynamic loading your
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system uses; see the chapters about building extension modules on
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|
\UNIX{} (chapter \ref{building-on-unix}) and Windows (chapter
|
|
\ref{building-on-windows}) for more information about this.
|
|
% XXX Add information about MacOS
|
|
|
|
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 of an unpacked source distribution, add
|
|
a line to the file \file{Modules/Setup.local} describing your file:
|
|
|
|
\begin{verbatim}
|
|
spam spammodule.o
|
|
\end{verbatim}
|
|
|
|
and rebuild the interpreter by running \program{make} in the toplevel
|
|
directory. You can also run \program{make} in the \file{Modules/}
|
|
subdirectory, but then you must first rebuild \file{Makefile}
|
|
there by running `\program{make} Makefile'. (This is necessary each
|
|
time you change the \file{Setup} file.)
|
|
|
|
If your module requires additional libraries to link with, these can
|
|
be listed on the line in the configuration file as well, for instance:
|
|
|
|
\begin{verbatim}
|
|
spam spammodule.o -lX11
|
|
\end{verbatim}
|
|
|
|
\section{Calling Python Functions from C
|
|
\label{callingPython}}
|
|
|
|
So far we have concentrated on making C functions callable from
|
|
Python. The reverse is also useful: calling Python functions from C.
|
|
This is especially the case for libraries that support so-called
|
|
``callback'' functions. If a C interface makes use of callbacks, the
|
|
equivalent Python often needs to provide a callback mechanism to the
|
|
Python programmer; the implementation will require calling the Python
|
|
callback functions from a C callback. Other uses are also imaginable.
|
|
|
|
Fortunately, the Python interpreter is easily called recursively, and
|
|
there is a standard interface to call a Python function. (I won't
|
|
dwell on how to call the Python parser with a particular string as
|
|
input --- if you're interested, have a look at the implementation of
|
|
the \programopt{-c} command line option in \file{Python/pythonmain.c}
|
|
from the Python source code.)
|
|
|
|
Calling a Python function is easy. First, the Python program must
|
|
somehow pass you the Python function object. You should provide a
|
|
function (or some other interface) to do this. When this function is
|
|
called, save a pointer to the Python function object (be careful to
|
|
\cfunction{Py_INCREF()} it!) in a global variable --- or wherever you
|
|
see fit. For example, the following function might be part of a module
|
|
definition:
|
|
|
|
\begin{verbatim}
|
|
static PyObject *my_callback = NULL;
|
|
|
|
static PyObject *
|
|
my_set_callback(dummy, args)
|
|
PyObject *dummy, *args;
|
|
{
|
|
PyObject *result = NULL;
|
|
PyObject *temp;
|
|
|
|
if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
|
|
if (!PyCallable_Check(temp)) {
|
|
PyErr_SetString(PyExc_TypeError, "parameter must be callable");
|
|
return NULL;
|
|
}
|
|
Py_XINCREF(temp); /* Add a reference to new callback */
|
|
Py_XDECREF(my_callback); /* Dispose of previous callback */
|
|
my_callback = temp; /* Remember new callback */
|
|
/* Boilerplate to return "None" */
|
|
Py_INCREF(Py_None);
|
|
result = Py_None;
|
|
}
|
|
return result;
|
|
}
|
|
\end{verbatim}
|
|
|
|
This function must be registered with the interpreter using the
|
|
\constant{METH_VARARGS} flag; this is described in section
|
|
\ref{methodTable}, ``The Module's Method Table and Initialization
|
|
Function.'' The \cfunction{PyArg_ParseTuple()} function and its
|
|
arguments are documented in section \ref{parseTuple}, ``Format Strings
|
|
for \cfunction{PyArg_ParseTuple()}.''
|
|
|
|
The macros \cfunction{Py_XINCREF()} and \cfunction{Py_XDECREF()}
|
|
increment/decrement the reference count of an object and are safe in
|
|
the presence of \NULL{} pointers (but note that \var{temp} will not be
|
|
\NULL{} in this context). More info on them in section
|
|
\ref{refcounts}, ``Reference Counts.''
|
|
|
|
Later, when it is time to call the function, you call the C function
|
|
\cfunction{PyEval_CallObject()}. This function has two arguments, both
|
|
pointers to arbitrary Python objects: the Python function, and the
|
|
argument list. The argument list must always be a tuple object, whose
|
|
length is the number of arguments. To call the Python function with
|
|
no arguments, pass an empty tuple; to call it with one argument, pass
|
|
a singleton tuple. \cfunction{Py_BuildValue()} returns a tuple when its
|
|
format string consists of zero or more format codes between
|
|
parentheses. For example:
|
|
|
|
\begin{verbatim}
|
|
int arg;
|
|
PyObject *arglist;
|
|
PyObject *result;
|
|
...
|
|
arg = 123;
|
|
...
|
|
/* Time to call the callback */
|
|
arglist = Py_BuildValue("(i)", arg);
|
|
result = PyEval_CallObject(my_callback, arglist);
|
|
Py_DECREF(arglist);
|
|
\end{verbatim}
|
|
|
|
\cfunction{PyEval_CallObject()} returns a Python object pointer: this is
|
|
the return value of the Python function. \cfunction{PyEval_CallObject()} is
|
|
``reference-count-neutral'' with respect to its arguments. In the
|
|
example a new tuple was created to serve as the argument list, which
|
|
is \cfunction{Py_DECREF()}-ed immediately after the call.
|
|
|
|
The return value of \cfunction{PyEval_CallObject()} is ``new'': either it
|
|
is a brand new object, or it is an existing object whose reference
|
|
count has been incremented. So, unless you want to save it in a
|
|
global variable, you should somehow \cfunction{Py_DECREF()} the result,
|
|
even (especially!) if you are not interested in its value.
|
|
|
|
Before you do this, however, it is important to check that the return
|
|
value isn't \NULL{}. If it is, the Python function terminated by
|
|
raising an exception. If the C code that called
|
|
\cfunction{PyEval_CallObject()} is called from Python, it should now
|
|
return an error indication to its Python caller, so the interpreter
|
|
can print a stack trace, or the calling Python code can handle the
|
|
exception. If this is not possible or desirable, the exception should
|
|
be cleared by calling \cfunction{PyErr_Clear()}. For example:
|
|
|
|
\begin{verbatim}
|
|
if (result == NULL)
|
|
return NULL; /* Pass error back */
|
|
...use result...
|
|
Py_DECREF(result);
|
|
\end{verbatim}
|
|
|
|
Depending on the desired interface to the Python callback function,
|
|
you may also have to provide an argument list to
|
|
\cfunction{PyEval_CallObject()}. In some cases the argument list is
|
|
also provided by the Python program, through the same interface that
|
|
specified the callback function. It can then be saved and used in the
|
|
same manner as the function object. In other cases, you may have to
|
|
construct a new tuple to pass as the argument list. The simplest way
|
|
to do this is to call \cfunction{Py_BuildValue()}. For example, if
|
|
you want to pass an integral event code, you might use the following
|
|
code:
|
|
|
|
\begin{verbatim}
|
|
PyObject *arglist;
|
|
...
|
|
arglist = Py_BuildValue("(l)", eventcode);
|
|
result = PyEval_CallObject(my_callback, arglist);
|
|
Py_DECREF(arglist);
|
|
if (result == NULL)
|
|
return NULL; /* Pass error back */
|
|
/* Here maybe use the result */
|
|
Py_DECREF(result);
|
|
\end{verbatim}
|
|
|
|
Note the placement of \samp{Py_DECREF(arglist)} immediately after the
|
|
call, before the error check! Also note that strictly spoken this
|
|
code is not complete: \cfunction{Py_BuildValue()} may run out of
|
|
memory, and this should be checked.
|
|
|
|
|
|
\section{Extracting Parameters in Extension Functions
|
|
\label{parseTuple}}
|
|
|
|
The \cfunction{PyArg_ParseTuple()} function is declared as follows:
|
|
|
|
\begin{verbatim}
|
|
int PyArg_ParseTuple(PyObject *arg, char *format, ...);
|
|
\end{verbatim}
|
|
|
|
The \var{arg} argument must be a tuple object containing an argument
|
|
list passed from Python to a C function. The \var{format} argument
|
|
must be a format string, whose syntax is explained 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 \cfunction{PyArg_ParseTuple()} checks that the Python
|
|
arguments have the required types, it cannot check the validity of the
|
|
addresses of C variables passed to the call: if you make mistakes
|
|
there, your code will probably crash or at least overwrite random bits
|
|
in memory. So be careful!
|
|
|
|
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 \cfunction{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.)
|
|
|
|
Note that any Python object references which are provided to the
|
|
caller are \emph{borrowed} references; do not decrement their
|
|
reference count!
|
|
|
|
\begin{description}
|
|
|
|
\item[\samp{s} (string or Unicode object) {[char *]}]
|
|
Convert a Python string or Unicode object 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 \exception{TypeError} exception is raised.
|
|
Unicode objects are converted to C strings using the default
|
|
encoding. If this conversion fails, an \exception{UnicodeError} is
|
|
raised.
|
|
|
|
\item[\samp{s\#} (string, Unicode or any read buffer compatible object)
|
|
{[char *, int]}]
|
|
This variant on \samp{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. Unicode
|
|
objects pass back a pointer to the default encoded string version of the
|
|
object if such a conversion is possible. All other read buffer
|
|
compatible objects pass back a reference to the raw internal data
|
|
representation.
|
|
|
|
\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 \NULL{}.
|
|
|
|
\item[\samp{z\#} (string or \code{None} or any read buffer compatible object)
|
|
{[char *, int]}]
|
|
This is to \samp{s\#} as \samp{z} is to \samp{s}.
|
|
|
|
\item[\samp{u} (Unicode object) {[Py_UNICODE *]}]
|
|
Convert a Python Unicode object to a C pointer to a null-terminated
|
|
buffer of 16-bit Unicode (UTF-16) data. As with \samp{s}, there is no need
|
|
to provide storage for the Unicode data buffer; a pointer to the
|
|
existing Unicode data is stored into the Py_UNICODE pointer variable whose
|
|
address you pass.
|
|
|
|
\item[\samp{u\#} (Unicode object) {[Py_UNICODE *, int]}]
|
|
This variant on \samp{u} stores into two C variables, the first one
|
|
a pointer to a Unicode data buffer, the second one its length.
|
|
|
|
\item[\samp{es} (string, Unicode object or character buffer compatible
|
|
object) {[const char *encoding, char **buffer]}]
|
|
This variant on \samp{s} is used for encoding Unicode and objects
|
|
convertible to Unicode into a character buffer. It only works for
|
|
encoded data without embedded \NULL{} bytes.
|
|
|
|
The variant reads one C variable and stores into two C variables, the
|
|
first one a pointer to an encoding name string (\var{encoding}), and the
|
|
second a pointer to a pointer to a character buffer (\var{**buffer},
|
|
the buffer used for storing the encoded data).
|
|
|
|
The encoding name must map to a registered codec. If set to \NULL{},
|
|
the default encoding is used.
|
|
|
|
\cfunction{PyArg_ParseTuple()} will allocate a buffer of the needed
|
|
size using \cfunction{PyMem_NEW()}, copy the encoded data into this
|
|
buffer and adjust \var{*buffer} to reference the newly allocated
|
|
storage. The caller is responsible for calling
|
|
\cfunction{PyMem_Free()} to free the allocated buffer after usage.
|
|
|
|
\item[\samp{et} (string, Unicode object or character buffer compatible
|
|
object) {[const char *encoding, char **buffer]}]
|
|
Same as \samp{es} except that string objects are passed through without
|
|
recoding them. Instead, the implementation assumes that the string
|
|
object uses the encoding passed in as parameter.
|
|
|
|
\item[\samp{es\#} (string, Unicode object or character buffer compatible
|
|
object) {[const char *encoding, char **buffer, int *buffer_length]}]
|
|
This variant on \samp{s\#} is used for encoding Unicode and objects
|
|
convertible to Unicode into a character buffer. It reads one C
|
|
variable and stores into three C variables, the first one a pointer to
|
|
an encoding name string (\var{encoding}), the second a pointer to a
|
|
pointer to a character buffer (\var{**buffer}, the buffer used for
|
|
storing the encoded data) and the third one a pointer to an integer
|
|
(\var{*buffer_length}, the buffer length).
|
|
|
|
The encoding name must map to a registered codec. If set to \NULL{},
|
|
the default encoding is used.
|
|
|
|
There are two modes of operation:
|
|
|
|
If \var{*buffer} points a \NULL{} pointer,
|
|
\cfunction{PyArg_ParseTuple()} will allocate a buffer of the needed
|
|
size using \cfunction{PyMem_NEW()}, copy the encoded data into this
|
|
buffer and adjust \var{*buffer} to reference the newly allocated
|
|
storage. The caller is responsible for calling
|
|
\cfunction{PyMem_Free()} to free the allocated buffer after usage.
|
|
|
|
If \var{*buffer} points to a non-\NULL{} pointer (an already allocated
|
|
buffer), \cfunction{PyArg_ParseTuple()} will use this location as
|
|
buffer and interpret \var{*buffer_length} as buffer size. It will then
|
|
copy the encoded data into the buffer and 0-terminate it. Buffer
|
|
overflow is signalled with an exception.
|
|
|
|
In both cases, \var{*buffer_length} is set to the length of the
|
|
encoded data without the trailing 0-byte.
|
|
|
|
\item[\samp{et\#} (string, Unicode object or character buffer compatible
|
|
object) {[const char *encoding, char **buffer]}]
|
|
Same as \samp{es\#} except that string objects are passed through without
|
|
recoding them. Instead, the implementation assumes that the string
|
|
object uses the encoding passed in as parameter.
|
|
|
|
\item[\samp{b} (integer) {[char]}]
|
|
Convert a Python integer to a tiny int, stored in a C \ctype{char}.
|
|
|
|
\item[\samp{h} (integer) {[short int]}]
|
|
Convert a Python integer to a C \ctype{short int}.
|
|
|
|
\item[\samp{i} (integer) {[int]}]
|
|
Convert a Python integer to a plain C \ctype{int}.
|
|
|
|
\item[\samp{l} (integer) {[long int]}]
|
|
Convert a Python integer to a C \ctype{long int}.
|
|
|
|
\item[\samp{c} (string of length 1) {[char]}]
|
|
Convert a Python character, represented as a string of length 1, to a
|
|
C \ctype{char}.
|
|
|
|
\item[\samp{f} (float) {[float]}]
|
|
Convert a Python floating point number to a C \ctype{float}.
|
|
|
|
\item[\samp{d} (float) {[double]}]
|
|
Convert a Python floating point number to a C \ctype{double}.
|
|
|
|
\item[\samp{D} (complex) {[Py_complex]}]
|
|
Convert a Python complex number to a C \ctype{Py_complex} structure.
|
|
|
|
\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
|
|
\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 \ctype{PyObject *}) into which the object pointer is stored.
|
|
If the Python object does not have the required type,
|
|
\exception{TypeError} 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 \ctype{void *}. The \var{converter} function in turn is called as
|
|
follows:
|
|
|
|
\var{status}\code{ = }\var{converter}\code{(}\var{object}, \var{address}\code{);}
|
|
|
|
where \var{object} is the Python object to be converted and
|
|
\var{address} is the \ctype{void *} argument that was passed to
|
|
\cfunction{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 requires that the Python object is a string object.
|
|
Raises \exception{TypeError} if the object is not a string object.
|
|
The C variable may also be declared as \ctype{PyObject *}.
|
|
|
|
\item[\samp{U} (Unicode string) {[PyUnicodeObject *]}]
|
|
Like \samp{O} but requires that the Python object is a Unicode object.
|
|
Raises \exception{TypeError} if the object is not a Unicode object.
|
|
The C variable may also be declared as \ctype{PyObject *}.
|
|
|
|
\item[\samp{t\#} (read-only character buffer) {[char *, int]}]
|
|
Like \samp{s\#}, but accepts any object which implements the read-only
|
|
buffer interface. The \ctype{char *} variable is set to point to the
|
|
first byte of the buffer, and the \ctype{int} is set to the length of
|
|
the buffer. Only single-segment buffer objects are accepted;
|
|
\exception{TypeError} is raised for all others.
|
|
|
|
\item[\samp{w} (read-write character buffer) {[char *]}]
|
|
Similar to \samp{s}, but accepts any object which implements the
|
|
read-write buffer interface. The caller must determine the length of
|
|
the buffer by other means, or use \samp{w\#} instead. Only
|
|
single-segment buffer objects are accepted; \exception{TypeError} is
|
|
raised for all others.
|
|
|
|
\item[\samp{w\#} (read-write character buffer) {[char *, int]}]
|
|
Like \samp{s\#}, but accepts any object which implements the
|
|
read-write buffer interface. The \ctype{char *} variable is set to
|
|
point to the first byte of the buffer, and the \ctype{int} is set to
|
|
the length of the buffer. Only single-segment buffer objects are
|
|
accepted; \exception{TypeError} is raised for all others.
|
|
|
|
\item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}]
|
|
The object must be a Python sequence 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 sequences
|
|
may be nested.
|
|
|
|
\strong{Note:} Prior to Python version 1.5.2, this format specifier
|
|
only accepted a tuple containing the individual parameters, not an
|
|
arbitrary sequence. Code which previously caused
|
|
\exception{TypeError} to be raised here may now proceed without an
|
|
exception. This is not expected to be a problem for existing code.
|
|
|
|
\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 mileage 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, \cfunction{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 exception that \cfunction{PyArg_ParseTuple()} raises).
|
|
|
|
\item[\samp{;}]
|
|
The list of format units ends here; the string after the semicolon 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:
|
|
|
|
\begin{verbatim}
|
|
int ok;
|
|
int i, j;
|
|
long k, l;
|
|
char *s;
|
|
int size;
|
|
|
|
ok = PyArg_ParseTuple(args, ""); /* No arguments */
|
|
/* Python call: f() */
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
ok = PyArg_ParseTuple(args, "s", &s); /* A string */
|
|
/* Possible Python call: f('whoops!') */
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
|
|
/* Possible Python call: f(1, 2, 'three') */
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
|
|
/* A pair of ints and a string, whose size is also returned */
|
|
/* Possible Python call: f((1, 2), 'three') */
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
{
|
|
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) */
|
|
}
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
{
|
|
int left, top, right, bottom, h, v;
|
|
ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
|
|
&left, &top, &right, &bottom, &h, &v);
|
|
/* A rectangle and a point */
|
|
/* Possible Python call:
|
|
f(((0, 0), (400, 300)), (10, 10)) */
|
|
}
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
{
|
|
Py_complex c;
|
|
ok = PyArg_ParseTuple(args, "D:myfunction", &c);
|
|
/* a complex, also providing a function name for errors */
|
|
/* Possible Python call: myfunction(1+2j) */
|
|
}
|
|
\end{verbatim}
|
|
|
|
|
|
\section{Keyword Parameters for Extension Functions
|
|
\label{parseTupleAndKeywords}}
|
|
|
|
The \cfunction{PyArg_ParseTupleAndKeywords()} function is declared as
|
|
follows:
|
|
|
|
\begin{verbatim}
|
|
int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
|
|
char *format, char **kwlist, ...);
|
|
\end{verbatim}
|
|
|
|
The \var{arg} and \var{format} parameters are identical to those of the
|
|
\cfunction{PyArg_ParseTuple()} function. The \var{kwdict} parameter
|
|
is the dictionary of keywords received as the third parameter from the
|
|
Python runtime. The \var{kwlist} parameter is a \NULL{}-terminated
|
|
list of strings which identify the parameters; the names are matched
|
|
with the type information from \var{format} from left to right.
|
|
|
|
\strong{Note:} Nested tuples cannot be parsed when using keyword
|
|
arguments! Keyword parameters passed in which are not present in the
|
|
\var{kwlist} will cause \exception{TypeError} to be raised.
|
|
|
|
Here is an example module which uses keywords, based on an example by
|
|
Geoff Philbrick (\email{philbrick@hks.com}):%
|
|
\index{Philbrick, Geoff}
|
|
|
|
\begin{verbatim}
|
|
#include <stdio.h>
|
|
#include "Python.h"
|
|
|
|
static PyObject *
|
|
keywdarg_parrot(self, args, keywds)
|
|
PyObject *self;
|
|
PyObject *args;
|
|
PyObject *keywds;
|
|
{
|
|
int voltage;
|
|
char *state = "a stiff";
|
|
char *action = "voom";
|
|
char *type = "Norwegian Blue";
|
|
|
|
static char *kwlist[] = {"voltage", "state", "action", "type", NULL};
|
|
|
|
if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
|
|
&voltage, &state, &action, &type))
|
|
return NULL;
|
|
|
|
printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
|
|
action, voltage);
|
|
printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);
|
|
|
|
Py_INCREF(Py_None);
|
|
|
|
return Py_None;
|
|
}
|
|
|
|
static PyMethodDef keywdarg_methods[] = {
|
|
/* The cast of the function is necessary since PyCFunction values
|
|
* only take two PyObject* parameters, and keywdarg_parrot() takes
|
|
* three.
|
|
*/
|
|
{"parrot", (PyCFunction)keywdarg_parrot, METH_VARARGS|METH_KEYWORDS},
|
|
{NULL, NULL} /* sentinel */
|
|
};
|
|
|
|
void
|
|
initkeywdarg()
|
|
{
|
|
/* Create the module and add the functions */
|
|
Py_InitModule("keywdarg", keywdarg_methods);
|
|
}
|
|
\end{verbatim}
|
|
|
|
|
|
\section{Building Arbitrary Values
|
|
\label{buildValue}}
|
|
|
|
This function is the counterpart to \cfunction{PyArg_ParseTuple()}. It is
|
|
declared as follows:
|
|
|
|
\begin{verbatim}
|
|
PyObject *Py_BuildValue(char *format, ...);
|
|
\end{verbatim}
|
|
|
|
It recognizes a set of format units similar to the ones recognized by
|
|
\cfunction{PyArg_ParseTuple()}, but the arguments (which are input to the
|
|
function, not output) must not be pointers, just values. It returns a
|
|
new Python object, suitable for returning from a C function called
|
|
from Python.
|
|
|
|
One difference with \cfunction{PyArg_ParseTuple()}: while the latter
|
|
requires its first argument to be a tuple (since Python argument lists
|
|
are always represented as tuples internally),
|
|
\cfunction{Py_BuildValue()} does not always build a tuple. It builds
|
|
a tuple only if its format string contains two or more format units.
|
|
If the format string is empty, it returns \code{None}; if it contains
|
|
exactly one format unit, it returns whatever object is described by
|
|
that format unit. To force it to return a tuple of size 0 or one,
|
|
parenthesize the format string.
|
|
|
|
When memory buffers are passed as parameters to supply data to build
|
|
objects, as for the \samp{s} and \samp{s\#} formats, the required data
|
|
is copied. Buffers provided by the caller are never referenced by the
|
|
objects created by \cfunction{Py_BuildValue()}. In other words, if
|
|
your code invokes \cfunction{malloc()} and passes the allocated memory
|
|
to \cfunction{Py_BuildValue()}, your code is responsible for
|
|
calling \cfunction{free()} for that memory once
|
|
\cfunction{Py_BuildValue()} returns.
|
|
|
|
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 \NULL{}, \code{None} is used.
|
|
|
|
\item[\samp{s\#} (string) {[char *, int]}]
|
|
Convert a C string and its length to a Python object. If the C string
|
|
pointer is \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{u} (Unicode string) {[Py_UNICODE *]}]
|
|
Convert a null-terminated buffer of Unicode (UCS-2) data to a Python
|
|
Unicode object. If the Unicode buffer pointer is \NULL,
|
|
\code{None} is returned.
|
|
|
|
\item[\samp{u\#} (Unicode string) {[Py_UNICODE *, int]}]
|
|
Convert a Unicode (UCS-2) data buffer and its length to a Python
|
|
Unicode object. If the Unicode buffer pointer is \NULL, the length
|
|
is ignored and \code{None} is returned.
|
|
|
|
\item[\samp{i} (integer) {[int]}]
|
|
Convert a plain C \ctype{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 \ctype{long int} to a Python integer object.
|
|
|
|
\item[\samp{c} (string of length 1) {[char]}]
|
|
Convert a C \ctype{int} representing a character to a Python string of
|
|
length 1.
|
|
|
|
\item[\samp{d} (float) {[double]}]
|
|
Convert a C \ctype{double} to a Python floating point number.
|
|
|
|
\item[\samp{f} (float) {[float]}]
|
|
Same as \samp{d}.
|
|
|
|
\item[\samp{D} (complex) {[Py_complex *]}]
|
|
Convert a C \ctype{Py_complex} structure to a Python complex number.
|
|
|
|
\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 \NULL{}
|
|
pointer, it is assumed that this was caused because the call producing
|
|
the argument found an error and set an exception. Therefore,
|
|
\cfunction{Py_BuildValue()} will return \NULL{} but won't raise an
|
|
exception. If no exception has been raised yet,
|
|
\cdata{PyExc_SystemError} is set.
|
|
|
|
\item[\samp{S} (object) {[PyObject *]}]
|
|
Same as \samp{O}.
|
|
|
|
\item[\samp{U} (object) {[PyObject *]}]
|
|
Same as \samp{O}.
|
|
|
|
\item[\samp{N} (object) {[PyObject *]}]
|
|
Same as \samp{O}, except it doesn't increment the reference count on
|
|
the object. Useful when the object is created by a call to an object
|
|
constructor in the argument list.
|
|
|
|
\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 \ctype{void *}) as its argument and should return a
|
|
``new'' Python object, or \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
|
|
\cdata{PyExc_SystemError} exception is raised and \NULL{} returned.
|
|
|
|
Examples (to the left the call, to the right the resulting Python value):
|
|
|
|
\begin{verbatim}
|
|
Py_BuildValue("") None
|
|
Py_BuildValue("i", 123) 123
|
|
Py_BuildValue("iii", 123, 456, 789) (123, 456, 789)
|
|
Py_BuildValue("s", "hello") 'hello'
|
|
Py_BuildValue("ss", "hello", "world") ('hello', 'world')
|
|
Py_BuildValue("s#", "hello", 4) 'hell'
|
|
Py_BuildValue("()") ()
|
|
Py_BuildValue("(i)", 123) (123,)
|
|
Py_BuildValue("(ii)", 123, 456) (123, 456)
|
|
Py_BuildValue("(i,i)", 123, 456) (123, 456)
|
|
Py_BuildValue("[i,i]", 123, 456) [123, 456]
|
|
Py_BuildValue("{s:i,s:i}",
|
|
"abc", 123, "def", 456) {'abc': 123, 'def': 456}
|
|
Py_BuildValue("((ii)(ii)) (ii)",
|
|
1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6))
|
|
\end{verbatim}
|
|
|
|
|
|
\section{Reference Counts
|
|
\label{refcounts}}
|
|
|
|
In languages like C or \Cpp{}, the programmer is responsible for
|
|
dynamic allocation and deallocation of memory on the heap. In C,
|
|
this is done using the functions \cfunction{malloc()} and
|
|
\cfunction{free()}. In \Cpp{}, the operators \keyword{new} and
|
|
\keyword{delete} are used with essentially the same meaning; they are
|
|
actually implemented using \cfunction{malloc()} and
|
|
\cfunction{free()}, so we'll restrict the following discussion to the
|
|
latter.
|
|
|
|
Every block of memory allocated with \cfunction{malloc()} should
|
|
eventually be returned to the pool of available memory by exactly one
|
|
call to \cfunction{free()}. It is important to call
|
|
\cfunction{free()} at the right time. If a block's address is
|
|
forgotten but \cfunction{free()} is not called for it, the memory it
|
|
occupies cannot be reused until the program terminates. This is
|
|
called a \dfn{memory leak}. On the other hand, if a program calls
|
|
\cfunction{free()} for a block and then continues to use the block, it
|
|
creates a conflict with re-use of the block through another
|
|
\cfunction{malloc()} call. This is called \dfn{using freed memory}.
|
|
It has the same bad consequences as referencing uninitialized data ---
|
|
core dumps, wrong results, mysterious crashes.
|
|
|
|
Common causes of memory leaks are unusual paths through the code. For
|
|
instance, a function may allocate a block of memory, do some
|
|
calculation, and then free the block again. Now a change in the
|
|
requirements for the function may add a test to the calculation that
|
|
detects an error condition and can return prematurely from the
|
|
function. It's easy to forget to free the allocated memory block when
|
|
taking this premature exit, especially when it is added later to the
|
|
code. Such leaks, once introduced, often go undetected for a long
|
|
time: the error exit is taken only in a small fraction of all calls,
|
|
and most modern machines have plenty of virtual memory, so the leak
|
|
only becomes apparent in a long-running process that uses the leaking
|
|
function frequently. Therefore, it's important to prevent leaks from
|
|
happening by having a coding convention or strategy that minimizes
|
|
this kind of errors.
|
|
|
|
Since Python makes heavy use of \cfunction{malloc()} and
|
|
\cfunction{free()}, it needs a strategy to avoid memory leaks as well
|
|
as the use of freed memory. The chosen method is called
|
|
\dfn{reference counting}. The principle is simple: every object
|
|
contains a counter, which is incremented when a reference to the
|
|
object is stored somewhere, and which is decremented when a reference
|
|
to it is deleted. When the counter reaches zero, the last reference
|
|
to the object has been deleted and the object is freed.
|
|
|
|
An alternative strategy is called \dfn{automatic garbage collection}.
|
|
(Sometimes, reference counting is also referred to as a garbage
|
|
collection strategy, hence my use of ``automatic'' to distinguish the
|
|
two.) The big advantage of automatic garbage collection is that the
|
|
user doesn't need to call \cfunction{free()} explicitly. (Another claimed
|
|
advantage is an improvement in speed or memory usage --- this is no
|
|
hard fact however.) The disadvantage is that for C, there is no
|
|
truly portable automatic garbage collector, while reference counting
|
|
can be implemented portably (as long as the functions \cfunction{malloc()}
|
|
and \cfunction{free()} are available --- which the C Standard guarantees).
|
|
Maybe some day a sufficiently portable automatic garbage collector
|
|
will be available for C. Until then, we'll have to live with
|
|
reference counts.
|
|
|
|
\subsection{Reference Counting in Python
|
|
\label{refcountsInPython}}
|
|
|
|
There are two macros, \code{Py_INCREF(x)} and \code{Py_DECREF(x)},
|
|
which handle the incrementing and decrementing of the reference count.
|
|
\cfunction{Py_DECREF()} also frees the object when the count reaches zero.
|
|
For flexibility, it doesn't call \cfunction{free()} directly --- rather, it
|
|
makes a call through a function pointer in the object's \dfn{type
|
|
object}. For this purpose (and others), every object also contains a
|
|
pointer to its type object.
|
|
|
|
The big question now remains: when to use \code{Py_INCREF(x)} and
|
|
\code{Py_DECREF(x)}? Let's first introduce some terms. Nobody
|
|
``owns'' an object; however, you can \dfn{own a reference} to an
|
|
object. An object's reference count is now defined as the number of
|
|
owned references to it. The owner of a reference is responsible for
|
|
calling \cfunction{Py_DECREF()} when the reference is no longer
|
|
needed. Ownership of a reference can be transferred. There are three
|
|
ways to dispose of an owned reference: pass it on, store it, or call
|
|
\cfunction{Py_DECREF()}. Forgetting to dispose of an owned reference
|
|
creates a memory leak.
|
|
|
|
It is also possible to \dfn{borrow}\footnote{The metaphor of
|
|
``borrowing'' a reference is not completely correct: the owner still
|
|
has a copy of the reference.} a reference to an object. The borrower
|
|
of a reference should not call \cfunction{Py_DECREF()}. The borrower must
|
|
not hold on to the object longer than the owner from which it was
|
|
borrowed. Using a borrowed reference after the owner has disposed of
|
|
it risks using freed memory and should be avoided
|
|
completely.\footnote{Checking that the reference count is at least 1
|
|
\strong{does not work} --- the reference count itself could be in
|
|
freed memory and may thus be reused for another object!}
|
|
|
|
The advantage of borrowing over owning a reference is that you don't
|
|
need to take care of disposing of the reference on all possible paths
|
|
through the code --- in other words, with a borrowed reference you
|
|
don't run the risk of leaking when a premature exit is taken. The
|
|
disadvantage of borrowing over leaking is that there are some subtle
|
|
situations where in seemingly correct code a borrowed reference can be
|
|
used after the owner from which it was borrowed has in fact disposed
|
|
of it.
|
|
|
|
A borrowed reference can be changed into an owned reference by calling
|
|
\cfunction{Py_INCREF()}. This does not affect the status of the owner from
|
|
which the reference was borrowed --- it creates a new owned reference,
|
|
and gives full owner responsibilities (i.e., the new owner must
|
|
dispose of the reference properly, as well as the previous owner).
|
|
|
|
|
|
\subsection{Ownership Rules
|
|
\label{ownershipRules}}
|
|
|
|
Whenever an object reference is passed into or out of a function, it
|
|
is part of the function's interface specification whether ownership is
|
|
transferred with the reference or not.
|
|
|
|
Most functions that return a reference to an object pass on ownership
|
|
with the reference. In particular, all functions whose function it is
|
|
to create a new object, e.g.\ \cfunction{PyInt_FromLong()} and
|
|
\cfunction{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,
|
|
\cfunction{PyInt_FromLong()} maintains a cache of popular values and can
|
|
return a reference to a cached item.
|
|
|
|
Many functions that extract objects from other objects also transfer
|
|
ownership with the reference, for instance
|
|
\cfunction{PyObject_GetAttrString()}. The picture is less clear, here,
|
|
however, since a few common routines are exceptions:
|
|
\cfunction{PyTuple_GetItem()}, \cfunction{PyList_GetItem()},
|
|
\cfunction{PyDict_GetItem()}, and \cfunction{PyDict_GetItemString()}
|
|
all return references that you borrow from the tuple, list or
|
|
dictionary.
|
|
|
|
The function \cfunction{PyImport_AddModule()} also returns a borrowed
|
|
reference, even though it may actually create the object it returns:
|
|
this is possible because an owned reference to the object is stored in
|
|
\code{sys.modules}.
|
|
|
|
When you pass an object reference into another function, in general,
|
|
the function borrows the reference from you --- if it needs to store
|
|
it, it will use \cfunction{Py_INCREF()} to become an independent
|
|
owner. There are exactly two important exceptions to this rule:
|
|
\cfunction{PyTuple_SetItem()} and \cfunction{PyList_SetItem()}. These
|
|
functions take over ownership of the item passed to them --- even if
|
|
they fail! (Note that \cfunction{PyDict_SetItem()} and friends don't
|
|
take over ownership --- they are ``normal.'')
|
|
|
|
When a C function is called from Python, it borrows references to its
|
|
arguments from the caller. The caller owns a reference to the object,
|
|
so the borrowed reference's lifetime is guaranteed until the function
|
|
returns. Only when such a borrowed reference must be stored or passed
|
|
on, it must be turned into an owned reference by calling
|
|
\cfunction{Py_INCREF()}.
|
|
|
|
The object reference returned from a C function that is called from
|
|
Python must be an owned reference --- ownership is tranferred from the
|
|
function to its caller.
|
|
|
|
|
|
\subsection{Thin Ice
|
|
\label{thinIce}}
|
|
|
|
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
|
|
\cfunction{Py_DECREF()} on an unrelated object while borrowing a
|
|
reference to a list item. For instance:
|
|
|
|
\begin{verbatim}
|
|
bug(PyObject *list) {
|
|
PyObject *item = PyList_GetItem(list, 0);
|
|
|
|
PyList_SetItem(list, 1, PyInt_FromLong(0L));
|
|
PyObject_Print(item, stdout, 0); /* BUG! */
|
|
}
|
|
\end{verbatim}
|
|
|
|
This function first borrows a reference to \code{list[0]}, then
|
|
replaces \code{list[1]} with the value \code{0}, and finally prints
|
|
the borrowed reference. Looks harmless, right? But it's not!
|
|
|
|
Let's follow the control flow into \cfunction{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 \method{__del__()} method. If this
|
|
class instance has a reference count of 1, disposing of it will call
|
|
its \method{__del__()} method.
|
|
|
|
Since it is written in Python, the \method{__del__()} method can execute
|
|
arbitrary Python code. Could it perhaps do something to invalidate
|
|
the reference to \code{item} in \cfunction{bug()}? You bet! Assuming
|
|
that the list passed into \cfunction{bug()} is accessible to the
|
|
\method{__del__()} method, it could execute a statement to the effect of
|
|
\samp{del list[0]}, and assuming this was the last reference to that
|
|
object, it would free the memory associated with it, thereby
|
|
invalidating \code{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:
|
|
|
|
\begin{verbatim}
|
|
no_bug(PyObject *list) {
|
|
PyObject *item = PyList_GetItem(list, 0);
|
|
|
|
Py_INCREF(item);
|
|
PyList_SetItem(list, 1, PyInt_FromLong(0L));
|
|
PyObject_Print(item, stdout, 0);
|
|
Py_DECREF(item);
|
|
}
|
|
\end{verbatim}
|
|
|
|
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 \method{__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
|
|
\code{Py_BEGIN_ALLOW_THREADS}, and to re-acquire it using
|
|
\code{Py_END_ALLOW_THREADS}. This is common around blocking I/O
|
|
calls, to let other threads use the CPU while waiting for the I/O to
|
|
complete. Obviously, the following function has the same problem as
|
|
the previous one:
|
|
|
|
\begin{verbatim}
|
|
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! */
|
|
}
|
|
\end{verbatim}
|
|
|
|
|
|
\subsection{NULL Pointers
|
|
\label{nullPointers}}
|
|
|
|
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'', i.e.\ when a
|
|
pointer that may be \NULL{} is received, e.g.\ from
|
|
\cfunction{malloc()} or from a function that may raise an exception.
|
|
|
|
The macros \cfunction{Py_INCREF()} and \cfunction{Py_DECREF()}
|
|
do not check for \NULL{} pointers --- however, their variants
|
|
\cfunction{Py_XINCREF()} and \cfunction{Py_XDECREF()} do.
|
|
|
|
The macros for checking for a particular object type
|
|
(\code{Py\var{type}_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 (\code{args} in the examples) is never
|
|
\NULL{} --- in fact it guarantees that it is always a tuple.\footnote{
|
|
These guarantees don't hold when you use the ``old'' style
|
|
calling convention --- this is still found in much existing code.}
|
|
|
|
It is a severe error to ever let a \NULL{} pointer ``escape'' to
|
|
the Python user.
|
|
|
|
% Frank Stajano:
|
|
% A pedagogically buggy example, along the lines of the previous listing,
|
|
% would be helpful here -- showing in more concrete terms what sort of
|
|
% actions could cause the problem. I can't very well imagine it from the
|
|
% description.
|
|
|
|
|
|
\section{Writing Extensions in \Cpp{}
|
|
\label{cplusplus}}
|
|
|
|
It is possible to write extension modules in \Cpp{}. 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 \Cpp{} compiler. Functions that will be called by the
|
|
Python interpreter (in particular, module initalization functions)
|
|
have to be declared using \code{extern "C"}.
|
|
It is unnecessary to enclose the Python header files in
|
|
\code{extern "C" \{...\}} --- they use this form already if the symbol
|
|
\samp{__cplusplus} is defined (all recent \Cpp{} compilers define this
|
|
symbol).
|
|
|
|
|
|
\section{Providing a C API for an Extension Module
|
|
\label{using-cobjects}}
|
|
\sectionauthor{Konrad Hinsen}{hinsen@cnrs-orleans.fr}
|
|
|
|
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 \keyword{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 (e.g.\ Windows), whereas others require an explicit list of
|
|
imported symbols at module link time (e.g.\ AIX), 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 \keyword{static}, except for the module's
|
|
initialization function, in order to avoid name clashes with other
|
|
extension modules (as discussed in section~\ref{methodTable}). And it
|
|
means that symbols that \emph{should} be accessible from other
|
|
extension modules must be exported in a different way.
|
|
|
|
Python provides a special mechanism to pass C-level information (i.e.
|
|
pointers) from one extension module to another one: CObjects.
|
|
A CObject is a Python data type which stores a pointer (\ctype{void
|
|
*}). CObjects 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 CObject.
|
|
|
|
There are many ways in which CObjects can be used to export the C API
|
|
of an extension module. Each name could get its own CObject, or all C
|
|
API pointers could be stored in an array whose address is published in
|
|
a CObject. 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.
|
|
|
|
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 \ctype{void} pointers which
|
|
becomes the value of a CObject. 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 \module{spam} module from
|
|
section~\ref{simpleExample}. The function \function{spam.system()}
|
|
does not call the C library function \cfunction{system()} directly,
|
|
but a function \cfunction{PySpam_System()}, which would of course do
|
|
something more complicated in reality (such as adding ``spam'' to
|
|
every command). This function \cfunction{PySpam_System()} is also
|
|
exported to other extension modules.
|
|
|
|
The function \cfunction{PySpam_System()} is a plain C function,
|
|
declared \keyword{static} like everything else:
|
|
|
|
\begin{verbatim}
|
|
static int
|
|
PySpam_System(command)
|
|
char *command;
|
|
{
|
|
return system(command);
|
|
}
|
|
\end{verbatim}
|
|
|
|
The function \cfunction{spam_system()} is modified in a trivial way:
|
|
|
|
\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 = PySpam_System(command);
|
|
return Py_BuildValue("i", sts);
|
|
}
|
|
\end{verbatim}
|
|
|
|
In the beginning of the module, right after the line
|
|
|
|
\begin{verbatim}
|
|
#include "Python.h"
|
|
\end{verbatim}
|
|
|
|
two more lines must be added:
|
|
|
|
\begin{verbatim}
|
|
#define SPAM_MODULE
|
|
#include "spammodule.h"
|
|
\end{verbatim}
|
|
|
|
The \code{\#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:
|
|
|
|
\begin{verbatim}
|
|
void
|
|
initspam()
|
|
{
|
|
PyObject *m;
|
|
static void *PySpam_API[PySpam_API_pointers];
|
|
PyObject *c_api_object;
|
|
|
|
m = Py_InitModule("spam", SpamMethods);
|
|
|
|
/* Initialize the C API pointer array */
|
|
PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;
|
|
|
|
/* Create a CObject containing the API pointer array's address */
|
|
c_api_object = PyCObject_FromVoidPtr((void *)PySpam_API, NULL);
|
|
|
|
if (c_api_object != NULL) {
|
|
/* Create a name for this object in the module's namespace */
|
|
PyObject *d = PyModule_GetDict(m);
|
|
|
|
PyDict_SetItemString(d, "_C_API", c_api_object);
|
|
Py_DECREF(c_api_object);
|
|
}
|
|
}
|
|
\end{verbatim}
|
|
|
|
Note that \code{PySpam_API} is declared \code{static}; otherwise
|
|
the pointer array would disappear when \code{initspam} terminates!
|
|
|
|
The bulk of the work is in the header file \file{spammodule.h},
|
|
which looks like this:
|
|
|
|
\begin{verbatim}
|
|
#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 (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])
|
|
|
|
#define import_spam() \
|
|
{ \
|
|
PyObject *module = PyImport_ImportModule("spam"); \
|
|
if (module != NULL) { \
|
|
PyObject *module_dict = PyModule_GetDict(module); \
|
|
PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); \
|
|
if (PyCObject_Check(c_api_object)) { \
|
|
PySpam_API = (void **)PyCObject_AsVoidPtr(c_api_object); \
|
|
} \
|
|
} \
|
|
}
|
|
|
|
#endif
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#endif /* !defined(Py_SPAMMODULE_H */
|
|
\end{verbatim}
|
|
|
|
All that a client module must do in order to have access to the
|
|
function \cfunction{PySpam_System()} is to call the function (or
|
|
rather macro) \cfunction{import_spam()} in its initialization
|
|
function:
|
|
|
|
\begin{verbatim}
|
|
void
|
|
initclient()
|
|
{
|
|
PyObject *m;
|
|
|
|
Py_InitModule("client", ClientMethods);
|
|
import_spam();
|
|
}
|
|
\end{verbatim}
|
|
|
|
The main disadvantage of this approach is that the file
|
|
\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 CObjects offer additional
|
|
functionality, which is especially useful for memory allocation and
|
|
deallocation of the pointer stored in a CObject. The details
|
|
are described in the \citetitle[../api/api.html]{Python/C API
|
|
Reference Manual} in the section ``CObjects'' and in the
|
|
implementation of CObjects (files \file{Include/cobject.h} and
|
|
\file{Objects/cobject.c} in the Python source code distribution).
|
|
|
|
|
|
\chapter{Defining New Types
|
|
\label{defining-new-types}}
|
|
\sectionauthor{Michael Hudson}{mwh21@cam.ac.uk}
|
|
|
|
As mentioned in the last chapter, Python allows the writer of an
|
|
extension module to define new types that can be manipulated from
|
|
Python code, much like strings and lists in core Python.
|
|
|
|
This is not hard; the code for all extension types follows a pattern,
|
|
but there are some details that you need to understand before you can
|
|
get started.
|
|
|
|
\section{The Basics
|
|
\label{dnt-basics}}
|
|
|
|
The Python runtime sees all Python objects as variables of type
|
|
\ctype{PyObject*}. A \ctype{PyObject} is not a very magnificent
|
|
object - it just contains the refcount and a pointer to the object's
|
|
``type object''. This is where the action is; the type object
|
|
determines which (C) functions get called when, for instance, an
|
|
attribute gets looked up on an object or it is multiplied by another
|
|
object. I call these C functions ``type methods'' to distinguish them
|
|
from things like \code{[].append} (which I will call ``object
|
|
methods'' when I get around to them).
|
|
|
|
So, if you want to define a new object type, you need to create a new
|
|
type object.
|
|
|
|
This sort of thing can only be explained by example, so here's a
|
|
minimal, but complete, module that defines a new type:
|
|
|
|
\begin{verbatim}
|
|
#include <Python.h>
|
|
|
|
staticforward PyTypeObject noddy_NoddyType;
|
|
|
|
typedef struct {
|
|
PyObject_HEAD
|
|
} noddy_NoddyObject;
|
|
|
|
static PyObject*
|
|
noddy_new_noddy(PyObject* self, PyObject* args)
|
|
{
|
|
noddy_NoddyObject* noddy;
|
|
|
|
if (!PyArg_ParseTuple(args,":new_noddy"))
|
|
return NULL;
|
|
|
|
noddy = PyObject_New(noddy_NoddyObject, &noddy_NoddyType);
|
|
|
|
return (PyObject*)noddy;
|
|
}
|
|
|
|
static void
|
|
noddy_noddy_dealloc(PyObject* self)
|
|
{
|
|
PyObject_Del(self);
|
|
}
|
|
|
|
static PyTypeObject noddy_NoddyType = {
|
|
PyObject_HEAD_INIT(NULL)
|
|
0,
|
|
"Noddy",
|
|
sizeof(noddy_NoddyObject),
|
|
0,
|
|
noddy_noddy_dealloc, /*tp_dealloc*/
|
|
0, /*tp_print*/
|
|
0, /*tp_getattr*/
|
|
0, /*tp_setattr*/
|
|
0, /*tp_compare*/
|
|
0, /*tp_repr*/
|
|
0, /*tp_as_number*/
|
|
0, /*tp_as_sequence*/
|
|
0, /*tp_as_mapping*/
|
|
0, /*tp_hash */
|
|
};
|
|
|
|
static PyMethodDef noddy_methods[] = {
|
|
{ "new_noddy", noddy_new_noddy, METH_VARARGS },
|
|
{NULL, NULL}
|
|
};
|
|
|
|
DL_EXPORT(void)
|
|
initnoddy(void)
|
|
{
|
|
noddy_NoddyType.ob_type = &PyType_Type;
|
|
|
|
Py_InitModule("noddy", noddy_methods);
|
|
}
|
|
\end{verbatim}
|
|
|
|
Now that's quite a bit to take in at once, but hopefully bits will
|
|
seem familiar from the last chapter.
|
|
|
|
The first bit that will be new is:
|
|
|
|
\begin{verbatim}
|
|
staticforward PyTypeObject noddy_NoddyType;
|
|
\end{verbatim}
|
|
|
|
This names the type object that will be defining further down in the
|
|
file. It can't be defined here because its definition has to refer to
|
|
functions that have no yet been defined, but we need to be able to
|
|
refer to it, hence the declaration.
|
|
|
|
The \code{staticforward} is required to placate various brain dead
|
|
compilers.
|
|
|
|
\begin{verbatim}
|
|
typedef struct {
|
|
PyObject_HEAD
|
|
} noddy_NoddyObject;
|
|
\end{verbatim}
|
|
|
|
This is what a Noddy object will contain. In this case nothing more
|
|
than every Python object contains - a refcount and a pointer to a type
|
|
object. These are the fields the \code{PyObject_HEAD} macro brings
|
|
in. The reason for the macro is to standardize the layout and to
|
|
enable special debugging fields to be brought in debug builds.
|
|
|
|
For contrast
|
|
|
|
\begin{verbatim}
|
|
typedef struct {
|
|
PyObject_HEAD
|
|
long ob_ival;
|
|
} PyIntObject;
|
|
\end{verbatim}
|
|
|
|
is the corresponding definition for standard Python integers.
|
|
|
|
Next up is:
|
|
|
|
\begin{verbatim}
|
|
static PyObject*
|
|
noddy_new_noddy(PyObject* self, PyObject* args)
|
|
{
|
|
noddy_NoddyObject* noddy;
|
|
|
|
if (!PyArg_ParseTuple(args,":new_noddy"))
|
|
return NULL;
|
|
|
|
noddy = PyObject_New(noddy_NoddyObject, &noddy_NoddyType);
|
|
|
|
return (PyObject*)noddy;
|
|
}
|
|
\end{verbatim}
|
|
|
|
This is in fact just a regular module function, as described in the
|
|
last chapter. The reason it gets special mention is that this is
|
|
where we create our Noddy object. Defining PyTypeObject structures is
|
|
all very well, but if there's no way to actually \emph{create} one
|
|
of the wretched things it is not going to do anyone much good.
|
|
|
|
Almost always, you create objects with a call of the form:
|
|
|
|
\begin{verbatim}
|
|
PyObject_New(<type>, &<type object>);
|
|
\end{verbatim}
|
|
|
|
This allocates the memory and then initializes the object (i.e.\ sets
|
|
the reference count to one, makes the \cdata{ob_type} pointer point at
|
|
the right place and maybe some other stuff, depending on build options).
|
|
You \emph{can} do these steps separately if you have some reason to
|
|
--- but at this level we don't bother.
|
|
|
|
We cast the return value to a \ctype{PyObject*} because that's what
|
|
the Python runtime expects. This is safe because of guarantees about
|
|
the layout of structures in the C standard, and is a fairly common C
|
|
programming trick. One could declare \cfunction{noddy_new_noddy} to
|
|
return a \ctype{noddy_NoddyObject*} and then put a cast in the
|
|
definition of \cdata{noddy_methods} further down the file --- it
|
|
doesn't make much difference.
|
|
|
|
Now a Noddy object doesn't do very much and so doesn't need to
|
|
implement many type methods. One you can't avoid is handling
|
|
deallocation, so we find
|
|
|
|
\begin{verbatim}
|
|
static void
|
|
noddy_noddy_dealloc(PyObject* self)
|
|
{
|
|
PyObject_Del(self);
|
|
}
|
|
\end{verbatim}
|
|
|
|
This is so short as to be self explanatory. This function will be
|
|
called when the reference count on a Noddy object reaches \code{0} (or
|
|
it is found as part of an unreachable cycle by the cyclic garbage
|
|
collector). \cfunction{PyObject_Del()} is what you call when you want
|
|
an object to go away. If a Noddy object held references to other
|
|
Python objects, one would decref them here.
|
|
|
|
Moving on, we come to the crunch --- the type object.
|
|
|
|
\begin{verbatim}
|
|
static PyTypeObject noddy_NoddyType = {
|
|
PyObject_HEAD_INIT(NULL)
|
|
0,
|
|
"Noddy",
|
|
sizeof(noddy_NoddyObject),
|
|
0,
|
|
noddy_noddy_dealloc, /*tp_dealloc*/
|
|
0, /*tp_print*/
|
|
0, /*tp_getattr*/
|
|
0, /*tp_setattr*/
|
|
0, /*tp_compare*/
|
|
0, /*tp_repr*/
|
|
0, /*tp_as_number*/
|
|
0, /*tp_as_sequence*/
|
|
0, /*tp_as_mapping*/
|
|
0, /*tp_hash */
|
|
};
|
|
\end{verbatim}
|
|
|
|
Now if you go and look up the definition of \ctype{PyTypeObject} in
|
|
\file{object.h} you'll see that it has many, many more fields that the
|
|
definition above. The remaining fields will be filled with zeros by
|
|
the C compiler, and it's common practice to not specify them
|
|
explicitly unless you need them.
|
|
|
|
This is so important that I'm going to pick the top of it apart still
|
|
further:
|
|
|
|
\begin{verbatim}
|
|
PyObject_HEAD_INIT(NULL)
|
|
\end{verbatim}
|
|
|
|
This line is a bit of a wart; what we'd like to write is:
|
|
|
|
\begin{verbatim}
|
|
PyObject_HEAD_INIT(&PyType_Type)
|
|
\end{verbatim}
|
|
|
|
as the type of a type object is ``type'', but this isn't strictly
|
|
conforming C and some compilers complain. So instead we fill in the
|
|
\cdata{ob_type} field of \cdata{noddy_NoddyType} at the earliest
|
|
oppourtunity --- in \cfunction{initnoddy()}.
|
|
|
|
\begin{verbatim}
|
|
0,
|
|
\end{verbatim}
|
|
|
|
XXX why does the type info struct start PyObject_*VAR*_HEAD??
|
|
|
|
\begin{verbatim}
|
|
"Noddy",
|
|
\end{verbatim}
|
|
|
|
The name of our type. This will appear in the default textual
|
|
representation of our objects and in some error messages, for example:
|
|
|
|
\begin{verbatim}
|
|
>>> "" + noddy.new_noddy()
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in ?
|
|
TypeError: cannot add type "Noddy" to string
|
|
\end{verbatim}
|
|
|
|
\begin{verbatim}
|
|
sizeof(noddy_NoddyObject),
|
|
\end{verbatim}
|
|
|
|
This is so that Python knows how much memory to allocate when you call
|
|
\cfunction{PyObject_New}.
|
|
|
|
\begin{verbatim}
|
|
0,
|
|
\end{verbatim}
|
|
|
|
This has to do with variable length objects like lists and strings.
|
|
Ignore for now...
|
|
|
|
Now we get into the type methods, the things that make your objects
|
|
different from the others. Of course, the Noddy object doesn't
|
|
implement many of these, but as mentioned above you have to implement
|
|
the deallocation function.
|
|
|
|
\begin{verbatim}
|
|
noddy_noddy_dealloc, /*tp_dealloc*/
|
|
\end{verbatim}
|
|
|
|
From here, all the type methods are nil so I won't go over them yet -
|
|
that's for the next section!
|
|
|
|
Everything else in the file should be familiar, except for this line
|
|
in \cfunction{initnoddy}:
|
|
|
|
\begin{verbatim}
|
|
noddy_NoddyType.ob_type = &PyType_Type;
|
|
\end{verbatim}
|
|
|
|
This was alluded to above --- the \cdata{noddy_NoddyType} object should
|
|
have type ``type'', but \code{\&PyType_Type} is not constant and so
|
|
can't be used in its initializer. To work around this, we patch it up
|
|
in the module initialization.
|
|
|
|
That's it! All that remains is to build it; put the above code in a
|
|
file called \file{noddymodule.c} and
|
|
|
|
\begin{verbatim}
|
|
from distutils.core import setup, Extension
|
|
setup(name = "noddy", version = "1.0",
|
|
ext_modules = [Extension("noddy", ["noddymodule.c"])])
|
|
\end{verbatim}
|
|
|
|
in a file called \file{setup.py}; then typing
|
|
|
|
\begin{verbatim}
|
|
$ python setup.py build%$
|
|
\end{verbatim}
|
|
|
|
at a shell should produce a file \file{noddy.so} in a subdirectory;
|
|
move to that directory and fire up Python --- you should be able to
|
|
\code{import noddy} and play around with Noddy objects.
|
|
|
|
That wasn't so hard, was it?
|
|
|
|
\section{Type Methods
|
|
\label{dnt-type-methods}}
|
|
|
|
This section aims to give a quick fly-by on the various type methods
|
|
you can implement and what they do.
|
|
|
|
Here is the definition of \ctype{PyTypeObject}, with some fields only
|
|
used in debug builds omitted:
|
|
|
|
\begin{verbatim}
|
|
typedef struct _typeobject {
|
|
PyObject_VAR_HEAD
|
|
char *tp_name; /* For printing */
|
|
int tp_basicsize, tp_itemsize; /* For allocation */
|
|
|
|
/* Methods to implement standard operations */
|
|
|
|
destructor tp_dealloc;
|
|
printfunc tp_print;
|
|
getattrfunc tp_getattr;
|
|
setattrfunc tp_setattr;
|
|
cmpfunc tp_compare;
|
|
reprfunc tp_repr;
|
|
|
|
/* Method suites for standard classes */
|
|
|
|
PyNumberMethods *tp_as_number;
|
|
PySequenceMethods *tp_as_sequence;
|
|
PyMappingMethods *tp_as_mapping;
|
|
|
|
/* More standard operations (here for binary compatibility) */
|
|
|
|
hashfunc tp_hash;
|
|
ternaryfunc tp_call;
|
|
reprfunc tp_str;
|
|
getattrofunc tp_getattro;
|
|
setattrofunc tp_setattro;
|
|
|
|
/* Functions to access object as input/output buffer */
|
|
PyBufferProcs *tp_as_buffer;
|
|
|
|
/* Flags to define presence of optional/expanded features */
|
|
long tp_flags;
|
|
|
|
char *tp_doc; /* Documentation string */
|
|
|
|
/* call function for all accessible objects */
|
|
traverseproc tp_traverse;
|
|
|
|
/* delete references to contained objects */
|
|
inquiry tp_clear;
|
|
|
|
/* rich comparisons */
|
|
richcmpfunc tp_richcompare;
|
|
|
|
/* weak reference enabler */
|
|
long tp_weaklistoffset;
|
|
|
|
} PyTypeObject;
|
|
\end{verbatim}
|
|
|
|
Now that's a \emph{lot} of methods. Don't worry too much though - if
|
|
you have a type you want to define, the chances are very good that you
|
|
will only implement a handful of these.
|
|
|
|
As you probably expect by now, I'm going to go over this line-by-line,
|
|
saying a word about each field as we get to it.
|
|
|
|
\begin{verbatim}
|
|
char *tp_name; /* For printing */
|
|
\end{verbatim}
|
|
|
|
The name of the type - as mentioned in the last section, this will
|
|
appear in various places, almost entirely for diagnostic purposes.
|
|
Try to choose something that will be helpful in such a situation!
|
|
|
|
\begin{verbatim}
|
|
int tp_basicsize, tp_itemsize; /* For allocation */
|
|
\end{verbatim}
|
|
|
|
These fields tell the runtime how much memory to allocate when new
|
|
objects of this typed are created. Python has some builtin support
|
|
for variable length structures (think: strings, lists) which is where
|
|
the \cdata{tp_itemsize} field comes in. This will be dealt with
|
|
later.
|
|
|
|
Now we come to the basic type methods - the ones most extension types
|
|
will implement.
|
|
|
|
\begin{verbatim}
|
|
destructor tp_dealloc;
|
|
printfunc tp_print;
|
|
getattrfunc tp_getattr;
|
|
setattrfunc tp_setattr;
|
|
cmpfunc tp_compare;
|
|
reprfunc tp_repr;
|
|
\end{verbatim}
|
|
|
|
|
|
%\section{Attributes \& Methods
|
|
% \label{dnt-attrs-and-meths}}
|
|
|
|
|
|
\chapter{Building C and \Cpp{} Extensions on \UNIX{}
|
|
\label{building-on-unix}}
|
|
|
|
\sectionauthor{Jim Fulton}{jim@Digicool.com}
|
|
|
|
|
|
%The make file make file, building C extensions on Unix
|
|
|
|
|
|
Starting in Python 1.4, Python provides a special make file for
|
|
building make files for building dynamically-linked extensions and
|
|
custom interpreters. The make file make file builds a make file
|
|
that reflects various system variables determined by configure when
|
|
the Python interpreter was built, so people building module's don't
|
|
have to resupply these settings. This vastly simplifies the process
|
|
of building extensions and custom interpreters on Unix systems.
|
|
|
|
The make file make file is distributed as the file
|
|
\file{Misc/Makefile.pre.in} in the Python source distribution. The
|
|
first step in building extensions or custom interpreters is to copy
|
|
this make file to a development directory containing extension module
|
|
source.
|
|
|
|
The make file make file, \file{Makefile.pre.in} uses metadata
|
|
provided in a file named \file{Setup}. The format of the \file{Setup}
|
|
file is the same as the \file{Setup} (or \file{Setup.dist}) file
|
|
provided in the \file{Modules/} directory of the Python source
|
|
distribution. The \file{Setup} file contains variable definitions:
|
|
|
|
\begin{verbatim}
|
|
EC=/projects/ExtensionClass
|
|
\end{verbatim}
|
|
|
|
and module description lines. It can also contain blank lines and
|
|
comment lines that start with \character{\#}.
|
|
|
|
A module description line includes a module name, source files,
|
|
options, variable references, and other input files, such
|
|
as libraries or object files. Consider a simple example:
|
|
|
|
\begin{verbatim}
|
|
ExtensionClass ExtensionClass.c
|
|
\end{verbatim}
|
|
|
|
This is the simplest form of a module definition line. It defines a
|
|
module, \module{ExtensionClass}, which has a single source file,
|
|
\file{ExtensionClass.c}.
|
|
|
|
This slightly more complex example uses an \strong{-I} option to
|
|
specify an include directory:
|
|
|
|
\begin{verbatim}
|
|
EC=/projects/ExtensionClass
|
|
cPersistence cPersistence.c -I$(EC)
|
|
\end{verbatim} % $ <-- bow to font lock
|
|
|
|
This example also illustrates the format for variable references.
|
|
|
|
For systems that support dynamic linking, the \file{Setup} file should
|
|
begin:
|
|
|
|
\begin{verbatim}
|
|
*shared*
|
|
\end{verbatim}
|
|
|
|
to indicate that the modules defined in \file{Setup} are to be built
|
|
as dynamically linked modules. A line containing only \samp{*static*}
|
|
can be used to indicate the subsequently listed modules should be
|
|
statically linked.
|
|
|
|
Here is a complete \file{Setup} file for building a
|
|
\module{cPersistent} module:
|
|
|
|
\begin{verbatim}
|
|
# Set-up file to build the cPersistence module.
|
|
# Note that the text should begin in the first column.
|
|
*shared*
|
|
|
|
# We need the path to the directory containing the ExtensionClass
|
|
# include file.
|
|
EC=/projects/ExtensionClass
|
|
cPersistence cPersistence.c -I$(EC)
|
|
\end{verbatim} % $ <-- bow to font lock
|
|
|
|
After the \file{Setup} file has been created, \file{Makefile.pre.in}
|
|
is run with the \samp{boot} target to create a make file:
|
|
|
|
\begin{verbatim}
|
|
make -f Makefile.pre.in boot
|
|
\end{verbatim}
|
|
|
|
This creates the file, Makefile. To build the extensions, simply
|
|
run the created make file:
|
|
|
|
\begin{verbatim}
|
|
make
|
|
\end{verbatim}
|
|
|
|
It's not necessary to re-run \file{Makefile.pre.in} if the
|
|
\file{Setup} file is changed. The make file automatically rebuilds
|
|
itself if the \file{Setup} file changes.
|
|
|
|
|
|
\section{Building Custom Interpreters \label{custom-interps}}
|
|
|
|
The make file built by \file{Makefile.pre.in} can be run with the
|
|
\samp{static} target to build an interpreter:
|
|
|
|
\begin{verbatim}
|
|
make static
|
|
\end{verbatim}
|
|
|
|
Any modules defined in the \file{Setup} file before the
|
|
\samp{*shared*} line will be statically linked into the interpreter.
|
|
Typically, a \samp{*shared*} line is omitted from the
|
|
\file{Setup} file when a custom interpreter is desired.
|
|
|
|
|
|
\section{Module Definition Options \label{module-defn-options}}
|
|
|
|
Several compiler options are supported:
|
|
|
|
\begin{tableii}{l|l}{programopt}{Option}{Meaning}
|
|
\lineii{-C}{Tell the C pre-processor not to discard comments}
|
|
\lineii{-D\var{name}=\var{value}}{Define a macro}
|
|
\lineii{-I\var{dir}}{Specify an include directory, \var{dir}}
|
|
\lineii{-L\var{dir}}{Specify a link-time library directory, \var{dir}}
|
|
\lineii{-R\var{dir}}{Specify a run-time library directory, \var{dir}}
|
|
\lineii{-l\var{lib}}{Link a library, \var{lib}}
|
|
\lineii{-U\var{name}}{Undefine a macro}
|
|
\end{tableii}
|
|
|
|
Other compiler options can be included (snuck in) by putting them
|
|
in variables.
|
|
|
|
Source files can include files with \file{.c}, \file{.C}, \file{.cc},
|
|
\file{.cpp}, \file{.cxx}, and \file{.c++} extensions.
|
|
|
|
Other input files include files with \file{.a}, \file{.o}, \file{.sl},
|
|
and \file{.so} extensions.
|
|
|
|
|
|
\section{Example \label{module-defn-example}}
|
|
|
|
Here is a more complicated example from \file{Modules/Setup.dist}:
|
|
|
|
\begin{verbatim}
|
|
GMP=/ufs/guido/src/gmp
|
|
mpz mpzmodule.c -I$(GMP) $(GMP)/libgmp.a
|
|
\end{verbatim}
|
|
|
|
which could also be written as:
|
|
|
|
\begin{verbatim}
|
|
mpz mpzmodule.c -I$(GMP) -L$(GMP) -lgmp
|
|
\end{verbatim}
|
|
|
|
|
|
\section{Distributing your extension modules
|
|
\label{distributing}}
|
|
|
|
There are two ways to distribute extension modules for others to use.
|
|
The way that allows the easiest cross-platform support is to use the
|
|
\module{distutils}\refstmodindex{distutils} package. The manual
|
|
\citetitle[../dist/dist.html]{Distributing Python Modules} contains
|
|
information on this approach. It is recommended that all new
|
|
extensions be distributed using this approach to allow easy building
|
|
and installation across platforms. Older extensions should migrate to
|
|
this approach as well.
|
|
|
|
What follows describes the older approach; there are still many
|
|
extensions which use this.
|
|
|
|
When distributing your extension modules in source form, make sure to
|
|
include a \file{Setup} file. The \file{Setup} file should be named
|
|
\file{Setup.in} in the distribution. The make file make file,
|
|
\file{Makefile.pre.in}, will copy \file{Setup.in} to \file{Setup} if
|
|
the person installing the extension doesn't do so manually.
|
|
Distributing a \file{Setup.in} file makes it easy for people to
|
|
customize the \file{Setup} file while keeping the original in
|
|
\file{Setup.in}.
|
|
|
|
It is a good idea to include a copy of \file{Makefile.pre.in} for
|
|
people who do not have a source distribution of Python.
|
|
|
|
Do not distribute a make file. People building your modules
|
|
should use \file{Makefile.pre.in} to build their own make file. A
|
|
\file{README} file included in the package should provide simple
|
|
instructions to perform the build.
|
|
|
|
|
|
\chapter{Building C and \Cpp{} Extensions on Windows
|
|
\label{building-on-windows}}
|
|
|
|
|
|
This chapter briefly explains how to create a Windows extension module
|
|
for Python using Microsoft Visual \Cpp{}, and follows with more
|
|
detailed background information on how it works. The explanatory
|
|
material is useful for both the Windows programmer learning to build
|
|
Python extensions and the \UNIX{} programmer interested in producing
|
|
software which can be successfully built on both \UNIX{} and Windows.
|
|
|
|
|
|
\section{A Cookbook Approach \label{win-cookbook}}
|
|
|
|
\sectionauthor{Neil Schemenauer}{neil_schemenauer@transcanada.com}
|
|
|
|
This section provides a recipe for building a Python extension on
|
|
Windows.
|
|
|
|
Grab the binary installer from \url{http://www.python.org/} and
|
|
install Python. The binary installer has all of the required header
|
|
files except for \file{config.h}.
|
|
|
|
Get the source distribution and extract it into a convenient location.
|
|
Copy the \file{config.h} from the \file{PC/} directory into the
|
|
\file{include/} directory created by the installer.
|
|
|
|
Create a \file{Setup} file for your extension module, as described in
|
|
chapter \ref{building-on-unix}.
|
|
|
|
Get David Ascher's \file{compile.py} script from
|
|
\url{http://starship.python.net/crew/da/compile/}. Run the script to
|
|
create Microsoft Visual \Cpp{} project files.
|
|
|
|
Open the DSW file in Visual \Cpp{} and select \strong{Build}.
|
|
|
|
If your module creates a new type, you may have trouble with this line:
|
|
|
|
\begin{verbatim}
|
|
PyObject_HEAD_INIT(&PyType_Type)
|
|
\end{verbatim}
|
|
|
|
Change it to:
|
|
|
|
\begin{verbatim}
|
|
PyObject_HEAD_INIT(NULL)
|
|
\end{verbatim}
|
|
|
|
and add the following to the module initialization function:
|
|
|
|
\begin{verbatim}
|
|
MyObject_Type.ob_type = &PyType_Type;
|
|
\end{verbatim}
|
|
|
|
Refer to section 3 of the
|
|
\citetitle[http://www.python.org/doc/FAQ.html]{Python FAQ} for details
|
|
on why you must do this.
|
|
|
|
|
|
\section{Differences Between \UNIX{} and Windows
|
|
\label{dynamic-linking}}
|
|
\sectionauthor{Chris Phoenix}{cphoenix@best.com}
|
|
|
|
|
|
\UNIX{} and Windows use completely different paradigms for run-time
|
|
loading of code. Before you try to build a module that can be
|
|
dynamically loaded, be aware of how your system works.
|
|
|
|
In \UNIX{}, a shared object (\file{.so}) file contains code to be used by the
|
|
program, and also the names of functions and data that it expects to
|
|
find in the program. When the file is joined to the program, all
|
|
references to those functions and data in the file's code are changed
|
|
to point to the actual locations in the program where the functions
|
|
and data are placed in memory. This is basically a link operation.
|
|
|
|
In Windows, a dynamic-link library (\file{.dll}) file has no dangling
|
|
references. Instead, an access to functions or data goes through a
|
|
lookup table. So the DLL code does not have to be fixed up at runtime
|
|
to refer to the program's memory; instead, the code already uses the
|
|
DLL's lookup table, and the lookup table is modified at runtime to
|
|
point to the functions and data.
|
|
|
|
In \UNIX{}, there is only one type of library file (\file{.a}) which
|
|
contains code from several object files (\file{.o}). During the link
|
|
step to create a shared object file (\file{.so}), the linker may find
|
|
that it doesn't know where an identifier is defined. The linker will
|
|
look for it in the object files in the libraries; if it finds it, it
|
|
will include all the code from that object file.
|
|
|
|
In Windows, there are two types of library, a static library and an
|
|
import library (both called \file{.lib}). A static library is like a
|
|
\UNIX{} \file{.a} file; it contains code to be included as necessary.
|
|
An import library is basically used only to reassure the linker that a
|
|
certain identifier is legal, and will be present in the program when
|
|
the DLL is loaded. So the linker uses the information from the
|
|
import library to build the lookup table for using identifiers that
|
|
are not included in the DLL. When an application or a DLL is linked,
|
|
an import library may be generated, which will need to be used for all
|
|
future DLLs that depend on the symbols in the application or DLL.
|
|
|
|
Suppose you are building two dynamic-load modules, B and C, which should
|
|
share another block of code A. On \UNIX{}, you would \emph{not} pass
|
|
\file{A.a} to the linker for \file{B.so} and \file{C.so}; that would
|
|
cause it to be included twice, so that B and C would each have their
|
|
own copy. In Windows, building \file{A.dll} will also build
|
|
\file{A.lib}. You \emph{do} pass \file{A.lib} to the linker for B and
|
|
C. \file{A.lib} does not contain code; it just contains information
|
|
which will be used at runtime to access A's code.
|
|
|
|
In Windows, using an import library is sort of like using \samp{import
|
|
spam}; it gives you access to spam's names, but does not create a
|
|
separate copy. On \UNIX{}, linking with a library is more like
|
|
\samp{from spam import *}; it does create a separate copy.
|
|
|
|
|
|
\section{Using DLLs in Practice \label{win-dlls}}
|
|
\sectionauthor{Chris Phoenix}{cphoenix@best.com}
|
|
|
|
Windows Python is built in Microsoft Visual \Cpp{}; using other
|
|
compilers may or may not work (though Borland seems to). The rest of
|
|
this section is MSV\Cpp{} specific.
|
|
|
|
When creating DLLs in Windows, you must pass \file{python15.lib} to
|
|
the linker. To build two DLLs, spam and ni (which uses C functions
|
|
found in spam), you could use these commands:
|
|
|
|
\begin{verbatim}
|
|
cl /LD /I/python/include spam.c ../libs/python15.lib
|
|
cl /LD /I/python/include ni.c spam.lib ../libs/python15.lib
|
|
\end{verbatim}
|
|
|
|
The first command created three files: \file{spam.obj},
|
|
\file{spam.dll} and \file{spam.lib}. \file{Spam.dll} does not contain
|
|
any Python functions (such as \cfunction{PyArg_ParseTuple()}), but it
|
|
does know how to find the Python code thanks to \file{python15.lib}.
|
|
|
|
The second command created \file{ni.dll} (and \file{.obj} and
|
|
\file{.lib}), which knows how to find the necessary functions from
|
|
spam, and also from the Python executable.
|
|
|
|
Not every identifier is exported to the lookup table. If you want any
|
|
other modules (including Python) to be able to see your identifiers,
|
|
you have to say \samp{_declspec(dllexport)}, as in \samp{void
|
|
_declspec(dllexport) initspam(void)} or \samp{PyObject
|
|
_declspec(dllexport) *NiGetSpamData(void)}.
|
|
|
|
Developer Studio will throw in a lot of import libraries that you do
|
|
not really need, adding about 100K to your executable. To get rid of
|
|
them, use the Project Settings dialog, Link tab, to specify
|
|
\emph{ignore default libraries}. Add the correct
|
|
\file{msvcrt\var{xx}.lib} to the list of libraries.
|
|
|
|
|
|
\chapter{Embedding Python in Another Application
|
|
\label{embedding}}
|
|
|
|
Embedding Python is similar to extending it, but not quite. The
|
|
difference is that when you extend Python, the main program of the
|
|
application is still the Python interpreter, while if you embed
|
|
Python, the main program may have nothing to do with Python ---
|
|
instead, some parts of the application occasionally call the Python
|
|
interpreter to run some Python code.
|
|
|
|
So if you are embedding Python, you are providing your own main
|
|
program. One of the things this main program has to do is initialize
|
|
the Python interpreter. At the very least, you have to call the
|
|
function \cfunction{Py_Initialize()} (on MacOS, call
|
|
\cfunction{PyMac_Initialize()} instead). There are optional calls to
|
|
pass command line arguments to Python. Then later you can call the
|
|
interpreter from any part of the application.
|
|
|
|
There are several different ways to call the interpreter: you can pass
|
|
a string containing Python statements to
|
|
\cfunction{PyRun_SimpleString()}, or you can pass a stdio file pointer
|
|
and a file name (for identification in error messages only) to
|
|
\cfunction{PyRun_SimpleFile()}. You can also call the lower-level
|
|
operations described in the previous chapters to construct and use
|
|
Python objects.
|
|
|
|
A simple demo of embedding Python can be found in the directory
|
|
\file{Demo/embed/} of the source distribution.
|
|
|
|
|
|
\section{Embedding Python in \Cpp{}
|
|
\label{embeddingInCplusplus}}
|
|
|
|
It is also possible to embed Python in a \Cpp{} program; precisely how this
|
|
is done will depend on the details of the \Cpp{} system used; in general you
|
|
will need to write the main program in \Cpp{}, and use the \Cpp{} compiler
|
|
to compile and link your program. There is no need to recompile Python
|
|
itself using \Cpp{}.
|
|
|
|
|
|
\section{Linking Requirements
|
|
\label{link-reqs}}
|
|
|
|
While the \program{configure} script shipped with the Python sources
|
|
will correctly build Python to export the symbols needed by
|
|
dynamically linked extensions, this is not automatically inherited by
|
|
applications which embed the Python library statically, at least on
|
|
\UNIX. This is an issue when the application is linked to the static
|
|
runtime library (\file{libpython.a}) and needs to load dynamic
|
|
extensions (implemented as \file{.so} files).
|
|
|
|
The problem is that some entry points are defined by the Python
|
|
runtime solely for extension modules to use. If the embedding
|
|
application does not use any of these entry points, some linkers will
|
|
not include those entries in the symbol table of the finished
|
|
executable. Some additional options are needed to inform the linker
|
|
not to remove these symbols.
|
|
|
|
Determining the right options to use for any given platform can be
|
|
quite difficult, but fortunately the Python configuration already has
|
|
those values. To retrieve them from an installed Python interpreter,
|
|
start an interactive interpreter and have a short session like this:
|
|
|
|
\begin{verbatim}
|
|
>>> import distutils.sysconfig
|
|
>>> distutils.sysconfig.get_config_var('LINKFORSHARED')
|
|
'-Xlinker -export-dynamic'
|
|
\end{verbatim}
|
|
\refstmodindex{distutils.sysconfig}
|
|
|
|
The contents of the string presented will be the options that should
|
|
be used. If the string is empty, there's no need to add any
|
|
additional options. The \constant{LINKFORSHARED} definition
|
|
corresponds to the variable of the same name in Python's top-level
|
|
\file{Makefile}.
|
|
|
|
|
|
\appendix
|
|
\chapter{Reporting Bugs}
|
|
\input{reportingbugs}
|
|
|
|
\end{document}
|