| \documentclass[twoside,openright]{report} |
| \usepackage{myformat} |
| |
| % XXX PM Modulator |
| |
| \title{Extending and Embedding the Python Interpreter} |
| |
| \input{boilerplate} |
| |
| % Tell \index to actually write the .idx file |
| \makeindex |
| |
| \begin{document} |
| |
| \maketitle |
| |
| \input{copyright} |
| |
| \begin{abstract} |
| |
| \noindent |
| Python is an interpreted, object-oriented programming language. This |
| document describes how to write modules in \C{} or \Cpp{} to extend the |
| Python interpreter with new modules. Those modules can define new |
| functions but also new object types and their methods. The document |
| also describes how to embed the Python interpreter in another |
| application, for use as an extension language. Finally, it shows how |
| to compile and link extension modules so that they can be loaded |
| dynamically (at run time) into the interpreter, if the underlying |
| operating system supports this feature. |
| |
| This document assumes basic knowledge about Python. For an informal |
| introduction to the language, see the Python Tutorial. The Python |
| Reference Manual gives a more formal definition of the language. The |
| Python Library Reference documents the existing object types, |
| functions and modules (both built-in and written in Python) that give |
| the language its wide application range. |
| |
| For a detailed description of the whole Python/\C{} API, see the separate |
| Python/\C{} API Reference Manual. \strong{Note:} While that manual is |
| still in a state of flux, it is safe to say that it is much more up to |
| date than the manual you're reading currently (which has been in need |
| for an upgrade for some time now). |
| |
| |
| \end{abstract} |
| |
| \tableofcontents |
| |
| |
| \chapter{Extending Python with \C{} or \Cpp{} code} |
| |
| |
| \section{Introduction} |
| |
| It is quite easy to add new built-in modules to Python, if you know |
| how to program in \C{}. Such \dfn{extension modules} can do two things |
| that can't be done directly in Python: they can implement new built-in |
| object types, and they can call \C{} library functions and system calls. |
| |
| To support extensions, the Python API (Application Programmers |
| Interface) defines a set of functions, macros and variables that |
| provide access to most aspects of the Python run-time system. The |
| Python API is incorporated in a \C{} source file by including the header |
| \code{"Python.h"}. |
| |
| The compilation of an extension module depends on its intended use as |
| well as on your system setup; details are given in a later section. |
| |
| |
| \section{A Simple Example} |
| |
| Let's create an extension module called \samp{spam} (the favorite food |
| of Monty Python fans...) and let's say we want to create a Python |
| interface to the \C{} library function \code{system()}.\footnote{An |
| interface for this function already exists in the standard module |
| \code{os} --- it was chosen as a simple and straightfoward example.} |
| This function takes a null-terminated character string as argument and |
| returns an integer. We want this function to be callable from Python |
| as follows: |
| |
| \begin{verbatim} |
| >>> import spam |
| >>> status = spam.system("ls -l") |
| \end{verbatim} |
| |
| Begin by creating a file \samp{spammodule.c}. (In general, if a |
| module is called \samp{spam}, the \C{} file containing its implementation |
| is called \file{spammodule.c}; if the module name is very long, like |
| \samp{spammify}, the module name can be just \file{spammify.c}.) |
| |
| The first line of our file can be: |
| |
| \begin{verbatim} |
| #include "Python.h" |
| \end{verbatim} |
| |
| which pulls in the Python API (you can add a comment describing the |
| purpose of the module and a copyright notice if you like). |
| |
| All user-visible symbols defined by \code{"Python.h"} have a prefix of |
| \samp{Py} or \samp{PY}, except those defined in standard header files. |
| For convenience, and since they are used extensively by the Python |
| interpreter, \code{"Python.h"} includes a few standard header files: |
| \code{<stdio.h>}, \code{<string.h>}, \code{<errno.h>}, and |
| \code{<stdlib.h>}. If the latter header file does not exist on your |
| system, it declares the functions \code{malloc()}, \code{free()} and |
| \code{realloc()} directly. |
| |
| The next thing we add to our module file is the \C{} function that will |
| be called when the Python expression \samp{spam.system(\var{string})} |
| is evaluated (we'll see shortly how it ends up being called): |
| |
| \begin{verbatim} |
| static PyObject * |
| spam_system(self, args) |
| PyObject *self; |
| PyObject *args; |
| { |
| char *command; |
| int sts; |
| if (!PyArg_ParseTuple(args, "s", &command)) |
| return NULL; |
| sts = system(command); |
| return Py_BuildValue("i", sts); |
| } |
| \end{verbatim} |
| |
| There is a straightforward translation from the argument list in |
| Python (e.g.\ the single expression \code{"ls -l"}) to the arguments |
| passed to the \C{} function. The \C{} function always has two arguments, |
| conventionally named \var{self} and \var{args}. |
| |
| The \var{self} argument is only used when the \C{} function implements a |
| builtin method. This will be discussed later. In the example, |
| \var{self} will always be a \NULL{} pointer, since we are defining |
| a function, not a method. (This is done so that the interpreter |
| doesn't have to understand two different types of \C{} functions.) |
| |
| The \var{args} argument will be a pointer to a Python tuple object |
| containing the arguments. Each item of the tuple corresponds to an |
| argument in the call's argument list. The arguments are Python |
| objects --- in order to do anything with them in our \C{} function we have |
| to convert them to \C{} values. The function \code{PyArg_ParseTuple()} |
| in the Python API checks the argument types and converts them to \C{} |
| values. It uses a template string to determine the required types of |
| the arguments as well as the types of the \C{} variables into which to |
| store the converted values. More about this later. |
| |
| \code{PyArg_ParseTuple()} returns true (nonzero) if all arguments have |
| the right type and its components have been stored in the variables |
| whose addresses are passed. It returns false (zero) if an invalid |
| argument list was passed. In the latter case it also raises an |
| appropriate exception by so the calling function can return |
| \NULL{} immediately (as we saw in the example). |
| |
| |
| \section{Intermezzo: Errors and Exceptions} |
| |
| An important convention throughout the Python interpreter is the |
| following: when a function fails, it should set an exception condition |
| and return an error value (usually a \NULL{} pointer). Exceptions |
| are stored in a static global variable inside the interpreter; if this |
| variable is \NULL{} no exception has occurred. A second global |
| variable stores the ``associated value'' of the exception (the second |
| argument to \code{raise}). A third variable contains the stack |
| traceback in case the error originated in Python code. These three |
| variables are the \C{} equivalents of the Python variables |
| \code{sys.exc_type}, \code{sys.exc_value} and \code{sys.exc_traceback} |
| (see the section on module \code{sys} in the Library Reference |
| Manual). It is important to know about them to understand how errors |
| are passed around. |
| |
| The Python API defines a number of functions to set various types of |
| exceptions. |
| |
| The most common one is \code{PyErr_SetString()}. Its arguments are an |
| exception object and a \C{} string. The exception object is usually a |
| predefined object like \code{PyExc_ZeroDivisionError}. The \C{} string |
| indicates the cause of the error and is converted to a Python string |
| object and stored as the ``associated value'' of the exception. |
| |
| Another useful function is \code{PyErr_SetFromErrno()}, which only |
| takes an exception argument and constructs the associated value by |
| inspection of the (\UNIX{}) global variable \code{errno}. The most |
| general function is \code{PyErr_SetObject()}, which takes two object |
| arguments, the exception and its associated value. You don't need to |
| \code{Py_INCREF()} the objects passed to any of these functions. |
| |
| You can test non-destructively whether an exception has been set with |
| \code{PyErr_Occurred()}. This returns the current exception object, |
| or \NULL{} if no exception has occurred. You normally don't need |
| to call \code{PyErr_Occurred()} to see whether an error occurred in a |
| function call, since you should be able to tell from the return value. |
| |
| When a function \var{f} that calls another function \var{g} detects |
| that the latter fails, \var{f} should itself return an error value |
| (e.g. \NULL{} or \code{-1}). It should \emph{not} call one of the |
| \code{PyErr_*()} functions --- one has already been called by \var{g}. |
| \var{f}'s caller is then supposed to also return an error indication |
| to \emph{its} caller, again \emph{without} calling \code{PyErr_*()}, |
| and so on --- the most detailed cause of the error was already |
| reported by the function that first detected it. Once the error |
| reaches the Python interpreter's main loop, this aborts the currently |
| executing Python code and tries to find an exception handler specified |
| by the Python programmer. |
| |
| (There are situations where a module can actually give a more detailed |
| error message by calling another \code{PyErr_*()} function, and in |
| such cases it is fine to do so. As a general rule, however, this is |
| not necessary, and can cause information about the cause of the error |
| to be lost: most operations can fail for a variety of reasons.) |
| |
| To ignore an exception set by a function call that failed, the exception |
| condition must be cleared explicitly by calling \code{PyErr_Clear()}. |
| The only time \C{} code should call \code{PyErr_Clear()} is if it doesn't |
| want to pass the error on to the interpreter but wants to handle it |
| completely by itself (e.g. by trying something else or pretending |
| nothing happened). |
| |
| Note that a failing \code{malloc()} call must be turned into an |
| exception --- the direct caller of \code{malloc()} (or |
| \code{realloc()}) must call \code{PyErr_NoMemory()} and return a |
| failure indicator itself. All the object-creating functions |
| (\code{PyInt_FromLong()} etc.) already do this, so only if you call |
| \code{malloc()} directly this note is of importance. |
| |
| Also note that, with the important exception of |
| \code{PyArg_ParseTuple()} and friends, functions that return an |
| integer status usually return a positive value or zero for success and |
| \code{-1} for failure, like \UNIX{} system calls. |
| |
| Finally, be careful to clean up garbage (by making \code{Py_XDECREF()} |
| or \code{Py_DECREF()} calls for objects you have already created) when |
| you return an error indicator! |
| |
| The choice of which exception to raise is entirely yours. There are |
| predeclared \C{} objects corresponding to all built-in Python exceptions, |
| e.g. \code{PyExc_ZeroDevisionError} which you can use directly. Of |
| course, you should choose exceptions wisely --- don't use |
| \code{PyExc_TypeError} to mean that a file couldn't be opened (that |
| should probably be \code{PyExc_IOError}). If something's wrong with |
| the argument list, the \code{PyArg_ParseTuple()} function usually |
| raises \code{PyExc_TypeError}. If you have an argument whose value |
| which must be in a particular range or must satisfy other conditions, |
| \code{PyExc_ValueError} is appropriate. |
| |
| You can also define a new exception that is unique to your module. |
| For this, you usually declare a static object variable at the |
| beginning of your file, e.g. |
| |
| \begin{verbatim} |
| static PyObject *SpamError; |
| \end{verbatim} |
| |
| and initialize it in your module's initialization function |
| (\code{initspam()}) with a string object, e.g. (leaving out the error |
| checking for now): |
| |
| \begin{verbatim} |
| void |
| initspam() |
| { |
| PyObject *m, *d; |
| m = Py_InitModule("spam", SpamMethods); |
| d = PyModule_GetDict(m); |
| SpamError = PyString_FromString("spam.error"); |
| PyDict_SetItemString(d, "error", SpamError); |
| } |
| \end{verbatim} |
| |
| Note that the Python name for the exception object is |
| \code{spam.error}. It is conventional for module and exception names |
| to be spelled in lower case. It is also conventional that the |
| \emph{value} of the exception object is the same as its name, e.g.\ |
| the string \code{"spam.error"}. |
| |
| |
| \section{Back to the Example} |
| |
| Going back to our example function, you should now be able to |
| understand this statement: |
| |
| \begin{verbatim} |
| if (!PyArg_ParseTuple(args, "s", &command)) |
| return NULL; |
| \end{verbatim} |
| |
| It returns \NULL{} (the error indicator for functions returning |
| object pointers) if an error is detected in the argument list, relying |
| on the exception set by \code{PyArg_ParseTuple()}. Otherwise the |
| string value of the argument has been copied to the local variable |
| \code{command}. This is a pointer assignment and you are not supposed |
| to modify the string to which it points (so in Standard \C{}, the variable |
| \code{command} should properly be declared as \samp{const char |
| *command}). |
| |
| The next statement is a call to the \UNIX{} function \code{system()}, |
| passing it the string we just got from \code{PyArg_ParseTuple()}: |
| |
| \begin{verbatim} |
| sts = system(command); |
| \end{verbatim} |
| |
| Our \code{spam.system()} function must return the value of \code{sts} |
| as a Python object. This is done using the function |
| \code{Py_BuildValue()}, which is something like the inverse of |
| \code{PyArg_ParseTuple()}: it takes a format string and an arbitrary |
| number of \C{} values, and returns a new Python object. More info on |
| \code{Py_BuildValue()} is given later. |
| |
| \begin{verbatim} |
| return Py_BuildValue("i", sts); |
| \end{verbatim} |
| |
| In this case, it will return an integer object. (Yes, even integers |
| are objects on the heap in Python!) |
| |
| If you have a \C{} function that returns no useful argument (a function |
| returning \code{void}), the corresponding Python function must return |
| \code{None}. You need this idiom to do so: |
| |
| \begin{verbatim} |
| Py_INCREF(Py_None); |
| return Py_None; |
| \end{verbatim} |
| |
| \code{Py_None} is the \C{} name for the special Python object |
| \code{None}. It is a genuine Python object (not a \NULL{} |
| pointer, which means ``error'' in most contexts, as we have seen). |
| |
| |
| \section{The Module's Method Table and Initialization Function} |
| |
| I promised to show how \code{spam_system()} is called from Python |
| programs. First, we need to list its name and address in a ``method |
| table'': |
| |
| \begin{verbatim} |
| static PyMethodDef SpamMethods[] = { |
| ... |
| {"system", spam_system, METH_VARARGS}, |
| ... |
| {NULL, NULL} /* Sentinel */ |
| }; |
| \end{verbatim} |
| |
| Note the third entry (\samp{METH_VARARGS}). This is a flag telling |
| the interpreter the calling convention to be used for the \C{} |
| function. It should normally always be \samp{METH_VARARGS} or |
| \samp{METH_VARARGS | METH_KEYWORDS}; a value of \samp{0} means that an |
| obsolete variant of \code{PyArg_ParseTuple()} is used. |
| |
| When using only \samp{METH_VARARGS}, the function should expect |
| the Python-level parameters to be passed in as a tuple acceptable for |
| parsing via \cfunction{PyArg_ParseTuple()}; more information on this |
| function is provided below. |
| |
| The \code{METH_KEYWORDS} bit may be set in the third field if keyword |
| arguments should be passed to the function. In this case, the \C{} |
| function should accept a third \samp{PyObject *} parameter which will |
| be a dictionary of keywords. Use \code{PyArg_ParseTupleAndKeywords()} |
| to parse the arguemts to such a function. |
| |
| The method table must be passed to the interpreter in the module's |
| initialization function (which should be the only non-\code{static} |
| item defined in the module file): |
| |
| \begin{verbatim} |
| void |
| initspam() |
| { |
| (void) Py_InitModule("spam", SpamMethods); |
| } |
| \end{verbatim} |
| |
| When the Python program imports module \code{spam} for the first time, |
| \code{initspam()} is called. It calls \code{Py_InitModule()}, which |
| creates a ``module object'' (which is inserted in the dictionary |
| \code{sys.modules} under the key \code{"spam"}), and inserts built-in |
| function objects into the newly created module based upon the table |
| (an array of \code{PyMethodDef} structures) that was passed as its |
| second argument. \code{Py_InitModule()} returns a pointer to the |
| module object that it creates (which is unused here). It aborts with |
| a fatal error if the module could not be initialized satisfactorily, |
| so the caller doesn't need to check for errors. |
| |
| |
| \section{Compilation and Linkage} |
| |
| There are two more things to do before you can use your new extension: |
| compiling and linking it with the Python system. If you use dynamic |
| loading, the details depend on the style of dynamic loading your |
| system uses; see the chapter on Dynamic Loading for more info about |
| this. |
| |
| If you can't use dynamic loading, or if you want to make your module a |
| permanent part of the Python interpreter, you will have to change the |
| configuration setup and rebuild the interpreter. Luckily, this is |
| very simple: just place your file (\file{spammodule.c} for example) in |
| the \file{Modules} directory, add a line to the file |
| \file{Modules/Setup} describing your file: |
| |
| \begin{verbatim} |
| spam spammodule.o |
| \end{verbatim} |
| |
| and rebuild the interpreter by running \code{make} in the toplevel |
| directory. You can also run \code{make} in the \file{Modules} |
| subdirectory, but then you must first rebuilt the \file{Makefile} |
| there by running \code{make Makefile}. (This is necessary each time |
| you change the \file{Setup} file.) |
| |
| If your module requires additional libraries to link with, these can |
| be listed on the line in the \file{Setup} file as well, for instance: |
| |
| \begin{verbatim} |
| spam spammodule.o -lX11 |
| \end{verbatim} |
| |
| \section{Calling Python Functions From \C{}} |
| |
| So far we have concentrated on making \C{} functions callable from |
| Python. The reverse is also useful: calling Python functions from \C{}. |
| This is especially the case for libraries that support so-called |
| ``callback'' functions. If a \C{} interface makes use of callbacks, the |
| equivalent Python often needs to provide a callback mechanism to the |
| Python programmer; the implementation will require calling the Python |
| callback functions from a \C{} callback. Other uses are also imaginable. |
| |
| Fortunately, the Python interpreter is easily called recursively, and |
| there is a standard interface to call a Python function. (I won't |
| dwell on how to call the Python parser with a particular string as |
| input --- if you're interested, have a look at the implementation of |
| the \samp{-c} command line option in \file{Python/pythonmain.c}.) |
| |
| Calling a Python function is easy. First, the Python program must |
| somehow pass you the Python function object. You should provide a |
| function (or some other interface) to do this. When this function is |
| called, save a pointer to the Python function object (be careful to |
| \code{Py_INCREF()} it!) in a global variable --- or whereever you see fit. |
| For example, the following function might be part of a module |
| definition: |
| |
| \begin{verbatim} |
| static PyObject *my_callback = NULL; |
| |
| static PyObject * |
| my_set_callback(dummy, arg) |
| PyObject *dummy, *arg; |
| { |
| Py_XDECREF(my_callback); /* Dispose of previous callback */ |
| Py_XINCREF(arg); /* Add a reference to new callback */ |
| my_callback = arg; /* Remember new callback */ |
| /* Boilerplate to return "None" */ |
| Py_INCREF(Py_None); |
| return Py_None; |
| } |
| \end{verbatim} |
| |
| The macros \code{Py_XINCREF()} and \code{Py_XDECREF()} increment/decrement |
| the reference count of an object and are safe in the presence of |
| \NULL{} pointers. More info on them in the section on Reference |
| Counts below. |
| |
| Later, when it is time to call the function, you call the \C{} function |
| \code{PyEval_CallObject()}. This function has two arguments, both |
| pointers to arbitrary Python objects: the Python function, and the |
| argument list. The argument list must always be a tuple object, whose |
| length is the number of arguments. To call the Python function with |
| no arguments, pass an empty tuple; to call it with one argument, pass |
| a singleton tuple. \code{Py_BuildValue()} returns a tuple when its |
| format string consists of zero or more format codes between |
| parentheses. For example: |
| |
| \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} |
| |
| \code{PyEval_CallObject()} returns a Python object pointer: this is |
| the return value of the Python function. \code{PyEval_CallObject()} is |
| ``reference-count-neutral'' with respect to its arguments. In the |
| example a new tuple was created to serve as the argument list, which |
| is \code{Py_DECREF()}-ed immediately after the call. |
| |
| The return value of \code{PyEval_CallObject()} is ``new'': either it |
| is a brand new object, or it is an existing object whose reference |
| count has been incremented. So, unless you want to save it in a |
| global variable, you should somehow \code{Py_DECREF()} the result, |
| even (especially!) if you are not interested in its value. |
| |
| Before you do this, however, it is important to check that the return |
| value isn't \NULL{}. If it is, the Python function terminated by raising |
| an exception. If the \C{} code that called \code{PyEval_CallObject()} is |
| called from Python, it should now return an error indication to its |
| Python caller, so the interpreter can print a stack trace, or the |
| calling Python code can handle the exception. If this is not possible |
| or desirable, the exception should be cleared by calling |
| \code{PyErr_Clear()}. For example: |
| |
| \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 \code{PyEval_CallObject()}. |
| In some cases the argument list is also provided by the Python |
| program, through the same interface that specified the callback |
| function. It can then be saved and used in the same manner as the |
| function object. In other cases, you may have to construct a new |
| tuple to pass as the argument list. The simplest way to do this is to |
| call \code{Py_BuildValue()}. For example, if you want to pass an integral |
| event code, you might use the following code: |
| |
| \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 \code{Py_DECREF(argument)} immediately after the call, |
| before the error check! Also note that strictly spoken this code is |
| not complete: \code{Py_BuildValue()} may run out of memory, and this should |
| be checked. |
| |
| |
| \section{Format Strings for \sectcode{PyArg_ParseTuple()}} |
| |
| The \code{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 \code{PyArg_ParseTuple()} checks that the Python |
| arguments have the required types, it cannot check the validity of the |
| addresses of \C{} variables passed to the call: if you make mistakes |
| there, your code will probably crash or at least overwrite random bits |
| in memory. So be careful! |
| |
| A format string consists of zero or more ``format units''. A format |
| unit describes one Python object; it is usually a single character or |
| a parenthesized sequence of format units. With a few exceptions, a |
| format unit that is not a parenthesized sequence normally corresponds |
| to a single address argument to \code{PyArg_ParseTuple()}. In the |
| following description, the quoted form is the format unit; the entry |
| in (round) parentheses is the Python object type that matches the |
| format unit; and the entry in [square] brackets is the type of the \C{} |
| variable(s) whose address should be passed. (Use the \samp{\&} |
| operator to pass a variable's address.) |
| |
| \begin{description} |
| |
| \item[\samp{s} (string) [char *{]}] |
| Convert a Python string to a \C{} pointer to a character string. You |
| must not provide storage for the string itself; a pointer to an |
| existing string is stored into the character pointer variable whose |
| address you pass. The \C{} string is null-terminated. The Python string |
| must not contain embedded null bytes; if it does, a \code{TypeError} |
| exception is raised. |
| |
| \item[\samp{s\#} (string) {[char *, int]}] |
| This variant on \code{'s'} stores into two \C{} variables, the first one |
| a pointer to a character string, the second one its length. In this |
| case the Python string may contain embedded null bytes. |
| |
| \item[\samp{z} (string or \code{None}) {[char *]}] |
| Like \samp{s}, but the Python object may also be \code{None}, in which |
| case the \C{} pointer is set to \NULL{}. |
| |
| \item[\samp{z\#} (string or \code{None}) {[char *, int]}] |
| This is to \code{'s\#'} as \code{'z'} is to \code{'s'}. |
| |
| \item[\samp{b} (integer) {[char]}] |
| Convert a Python integer to a tiny int, stored in a \C{} \code{char}. |
| |
| \item[\samp{h} (integer) {[short int]}] |
| Convert a Python integer to a \C{} \code{short int}. |
| |
| \item[\samp{i} (integer) {[int]}] |
| Convert a Python integer to a plain \C{} \code{int}. |
| |
| \item[\samp{l} (integer) {[long int]}] |
| Convert a Python integer to a \C{} \code{long int}. |
| |
| \item[\samp{c} (string of length 1) {[char]}] |
| Convert a Python character, represented as a string of length 1, to a |
| \C{} \code{char}. |
| |
| \item[\samp{f} (float) {[float]}] |
| Convert a Python floating point number to a \C{} \code{float}. |
| |
| \item[\samp{d} (float) {[double]}] |
| Convert a Python floating point number to a \C{} \code{double}. |
| |
| \item[\samp{D} (complex) {[Py_complex]}] |
| Convert a Python complex number to a \C{} \code{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 \code{PyObject *}) into which the object pointer is stored. |
| If the Python object does not have the required type, a |
| \code{TypeError} exception is raised. |
| |
| \item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}] |
| Convert a Python object to a \C{} variable through a \var{converter} |
| function. This takes two arguments: the first is a function, the |
| second is the address of a \C{} variable (of arbitrary type), converted |
| to \code{void *}. The \var{converter} function in turn is called as |
| follows: |
| |
| \code{\var{status} = \var{converter}(\var{object}, \var{address});} |
| |
| where \var{object} is the Python object to be converted and |
| \var{address} is the \code{void *} argument that was passed to |
| \code{PyArg_ConvertTuple()}. The returned \var{status} should be |
| \code{1} for a successful conversion and \code{0} if the conversion |
| has failed. When the conversion fails, the \var{converter} function |
| should raise an exception. |
| |
| \item[\samp{S} (string) {[PyStringObject *]}] |
| Like \samp{O} but requires that the Python object is a string object. |
| Raises a \code{TypeError} exception if the object is not a string |
| object. The \C{} variable may also be declared as \code{PyObject *}. |
| |
| \item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}] |
| The object must be a Python tuple whose length is the number of format |
| units in \var{items}. The \C{} arguments must correspond to the |
| individual format units in \var{items}. Format units for tuples may |
| be nested. |
| |
| \end{description} |
| |
| It is possible to pass Python long integers where integers are |
| requested; however no proper range checking is done --- the most |
| significant bits are silently truncated when the receiving field is |
| too small to receive the value (actually, the semantics are inherited |
| from downcasts in \C{} --- your milage may vary). |
| |
| A few other characters have a meaning in a format string. These may |
| not occur inside nested parentheses. They are: |
| |
| \begin{description} |
| |
| \item[\samp{|}] |
| Indicates that the remaining arguments in the Python argument list are |
| optional. The \C{} variables corresponding to optional arguments should |
| be initialized to their default value --- when an optional argument is |
| not specified, the \code{PyArg_ParseTuple} does not touch the contents |
| of the corresponding \C{} variable(s). |
| |
| \item[\samp{:}] |
| The list of format units ends here; the string after the colon is used |
| as the function name in error messages (the ``associated value'' of |
| the exceptions that \code{PyArg_ParseTuple} raises). |
| |
| \item[\samp{;}] |
| The list of format units ends here; the string after the colon is used |
| as the error message \emph{instead} of the default error message. |
| Clearly, \samp{:} and \samp{;} mutually exclude each other. |
| |
| \end{description} |
| |
| Some example calls: |
| |
| \begin{verbatim} |
| int ok; |
| int i, j; |
| long k, l; |
| char *s; |
| int size; |
| |
| ok = PyArg_ParseTuple(args, ""); /* No arguments */ |
| /* Python call: f() */ |
| |
| ok = PyArg_ParseTuple(args, "s", &s); /* A string */ |
| /* Possible Python call: f('whoops!') */ |
| |
| ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */ |
| /* Possible Python call: f(1, 2, 'three') */ |
| |
| ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size); |
| /* A pair of ints and a string, whose size is also returned */ |
| /* Possible Python call: f((1, 2), 'three') */ |
| |
| { |
| char *file; |
| char *mode = "r"; |
| int bufsize = 0; |
| ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize); |
| /* A string, and optionally another string and an integer */ |
| /* Possible Python calls: |
| f('spam') |
| f('spam', 'w') |
| f('spam', 'wb', 100000) */ |
| } |
| |
| { |
| int left, top, right, bottom, h, v; |
| ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)", |
| &left, &top, &right, &bottom, &h, &v); |
| /* A rectangle and a point */ |
| /* Possible Python call: |
| f(((0, 0), (400, 300)), (10, 10)) */ |
| } |
| |
| { |
| 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 Parsing with \sectcode{PyArg_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 a \exception{TypeError} to be raised. |
| |
| Here is an example module which uses keywords, based on an example by |
| Geoff Philbrick (\email{philbrick@hks.com}): |
| |
| \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[] = { |
| {"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{The \sectcode{Py_BuildValue()} Function} |
| |
| This function is the counterpart to \code{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 |
| \code{PyArg_ParseTuple()}, but the arguments (which are input to the |
| function, not output) must not be pointers, just values. It returns a |
| new Python object, suitable for returning from a \C{} function called |
| from Python. |
| |
| One difference with \code{PyArg_ParseTuple()}: while the latter |
| requires its first argument to be a tuple (since Python argument lists |
| are always represented as tuples internally), \code{BuildValue()} does |
| not always build a tuple. It builds a tuple only if its format string |
| contains two or more format units. If the format string is empty, it |
| returns \code{None}; if it contains exactly one format unit, it |
| returns whatever object is described by that format unit. To force it |
| to return a tuple of size 0 or one, parenthesize the format string. |
| |
| In the following description, the quoted form is the format unit; the |
| entry in (round) parentheses is the Python object type that the format |
| unit will return; and the entry in [square] brackets is the type of |
| the \C{} value(s) to be passed. |
| |
| The characters space, tab, colon and comma are ignored in format |
| strings (but not within format units such as \samp{s\#}). This can be |
| used to make long format strings a tad more readable. |
| |
| \begin{description} |
| |
| \item[\samp{s} (string) {[char *]}] |
| Convert a null-terminated \C{} string to a Python object. If the \C{} |
| string pointer is \NULL{}, \code{None} is returned. |
| |
| \item[\samp{s\#} (string) {[char *, int]}] |
| Convert a \C{} string and its length to a Python object. If the \C{} string |
| pointer is \NULL{}, the length is ignored and \code{None} is |
| returned. |
| |
| \item[\samp{z} (string or \code{None}) {[char *]}] |
| Same as \samp{s}. |
| |
| \item[\samp{z\#} (string or \code{None}) {[char *, int]}] |
| Same as \samp{s\#}. |
| |
| \item[\samp{i} (integer) {[int]}] |
| Convert a plain \C{} \code{int} to a Python integer object. |
| |
| \item[\samp{b} (integer) {[char]}] |
| Same as \samp{i}. |
| |
| \item[\samp{h} (integer) {[short int]}] |
| Same as \samp{i}. |
| |
| \item[\samp{l} (integer) {[long int]}] |
| Convert a \C{} \code{long int} to a Python integer object. |
| |
| \item[\samp{c} (string of length 1) {[char]}] |
| Convert a \C{} \code{int} representing a character to a Python string of |
| length 1. |
| |
| \item[\samp{d} (float) {[double]}] |
| Convert a \C{} \code{double} to a Python floating point number. |
| |
| \item[\samp{f} (float) {[float]}] |
| Same as \samp{d}. |
| |
| \item[\samp{O} (object) {[PyObject *]}] |
| Pass a Python object untouched (except for its reference count, which |
| is incremented by one). If the object passed in is a \NULL{} |
| pointer, it is assumed that this was caused because the call producing |
| the argument found an error and set an exception. Therefore, |
| \code{Py_BuildValue()} will return \NULL{} but won't raise an |
| exception. If no exception has been raised yet, |
| \code{PyExc_SystemError} is set. |
| |
| \item[\samp{S} (object) {[PyObject *]}] |
| Same as \samp{O}. |
| |
| \item[\samp{O\&} (object) {[\var{converter}, \var{anything}]}] |
| Convert \var{anything} to a Python object through a \var{converter} |
| function. The function is called with \var{anything} (which should be |
| compatible with \code{void *}) as its argument and should return a |
| ``new'' Python object, or \NULL{} if an error occurred. |
| |
| \item[\samp{(\var{items})} (tuple) {[\var{matching-items}]}] |
| Convert a sequence of \C{} values to a Python tuple with the same number |
| of items. |
| |
| \item[\samp{[\var{items}]} (list) {[\var{matching-items}]}] |
| Convert a sequence of \C{} values to a Python list with the same number |
| of items. |
| |
| \item[\samp{\{\var{items}\}} (dictionary) {[\var{matching-items}]}] |
| Convert a sequence of \C{} values to a Python dictionary. Each pair of |
| consecutive \C{} values adds one item to the dictionary, serving as key |
| and value, respectively. |
| |
| \end{description} |
| |
| If there is an error in the format string, the |
| \code{PyExc_SystemError} exception is raised and \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} |
| |
| \subsection{Introduction} |
| |
| In languages like \C{} or \Cpp{}, the programmer is responsible for |
| dynamic allocation and deallocation of memory on the heap. In \C{}, this |
| is done using the functions \code{malloc()} and \code{free()}. In |
| \Cpp{}, the operators \code{new} and \code{delete} are used with |
| essentially the same meaning; they are actually implemented using |
| \code{malloc()} and \code{free()}, so we'll restrict the following |
| discussion to the latter. |
| |
| Every block of memory allocated with \code{malloc()} should eventually |
| be returned to the pool of available memory by exactly one call to |
| \code{free()}. It is important to call \code{free()} at the right |
| time. If a block's address is forgotten but \code{free()} is not |
| called for it, the memory it occupies cannot be reused until the |
| program terminates. This is called a \dfn{memory leak}. On the other |
| hand, if a program calls \code{free()} for a block and then continues |
| to use the block, it creates a conflict with re-use of the block |
| through another \code{malloc()} call. This is called \dfn{using freed |
| memory}. It has the same bad consequences as referencing uninitialized |
| data --- core dumps, wrong results, mysterious crashes. |
| |
| Common causes of memory leaks are unusual paths through the code. For |
| instance, a function may allocate a block of memory, do some |
| calculation, and then free the block again. Now a change in the |
| requirements for the function may add a test to the calculation that |
| detects an error condition and can return prematurely from the |
| function. It's easy to forget to free the allocated memory block when |
| taking this premature exit, especially when it is added later to the |
| code. Such leaks, once introduced, often go undetected for a long |
| time: the error exit is taken only in a small fraction of all calls, |
| and most modern machines have plenty of virtual memory, so the leak |
| only becomes apparent in a long-running process that uses the leaking |
| function frequently. Therefore, it's important to prevent leaks from |
| happening by having a coding convention or strategy that minimizes |
| this kind of errors. |
| |
| Since Python makes heavy use of \code{malloc()} and \code{free()}, it |
| needs a strategy to avoid memory leaks as well as the use of freed |
| memory. The chosen method is called \dfn{reference counting}. The |
| principle is simple: every object contains a counter, which is |
| incremented when a reference to the object is stored somewhere, and |
| which is decremented when a reference to it is deleted. When the |
| counter reaches zero, the last reference to the object has been |
| deleted and the object is freed. |
| |
| An alternative strategy is called \dfn{automatic garbage collection}. |
| (Sometimes, reference counting is also referred to as a garbage |
| collection strategy, hence my use of ``automatic'' to distinguish the |
| two.) The big advantage of automatic garbage collection is that the |
| user doesn't need to call \code{free()} explicitly. (Another claimed |
| advantage is an improvement in speed or memory usage --- this is no |
| hard fact however.) The disadvantage is that for \C{}, there is no |
| truly portable automatic garbage collector, while reference counting |
| can be implemented portably (as long as the functions \code{malloc()} |
| and \code{free()} are available --- which the \C{} Standard guarantees). |
| Maybe some day a sufficiently portable automatic garbage collector |
| will be available for \C{}. Until then, we'll have to live with |
| reference counts. |
| |
| \subsection{Reference Counting in Python} |
| |
| There are two macros, \code{Py_INCREF(x)} and \code{Py_DECREF(x)}, |
| which handle the incrementing and decrementing of the reference count. |
| \code{Py_DECREF()} also frees the object when the count reaches zero. |
| For flexibility, it doesn't call \code{free()} directly --- rather, it |
| makes a call through a function pointer in the object's \dfn{type |
| object}. For this purpose (and others), every object also contains a |
| pointer to its type object. |
| |
| The big question now remains: when to use \code{Py_INCREF(x)} and |
| \code{Py_DECREF(x)}? Let's first introduce some terms. Nobody |
| ``owns'' an object; however, you can \dfn{own a reference} to an |
| object. An object's reference count is now defined as the number of |
| owned references to it. The owner of a reference is responsible for |
| calling \code{Py_DECREF()} when the reference is no longer needed. |
| Ownership of a reference can be transferred. There are three ways to |
| dispose of an owned reference: pass it on, store it, or call |
| \code{Py_DECREF()}. Forgetting to dispose of an owned reference creates |
| a memory leak. |
| |
| It is also possible to \dfn{borrow}\footnote{The metaphor of |
| ``borrowing'' a reference is not completely correct: the owner still |
| has a copy of the reference.} a reference to an object. The borrower |
| of a reference should not call \code{Py_DECREF()}. The borrower must |
| not hold on to the object longer than the owner from which it was |
| borrowed. Using a borrowed reference after the owner has disposed of |
| it risks using freed memory and should be avoided |
| completely.\footnote{Checking that the reference count is at least 1 |
| \strong{does not work} --- the reference count itself could be in |
| freed memory and may thus be reused for another object!} |
| |
| The advantage of borrowing over owning a reference is that you don't |
| need to take care of disposing of the reference on all possible paths |
| through the code --- in other words, with a borrowed reference you |
| don't run the risk of leaking when a premature exit is taken. The |
| disadvantage of borrowing over leaking is that there are some subtle |
| situations where in seemingly correct code a borrowed reference can be |
| used after the owner from which it was borrowed has in fact disposed |
| of it. |
| |
| A borrowed reference can be changed into an owned reference by calling |
| \code{Py_INCREF()}. This does not affect the status of the owner from |
| which the reference was borrowed --- it creates a new owned reference, |
| and gives full owner responsibilities (i.e., the new owner must |
| dispose of the reference properly, as well as the previous owner). |
| |
| \subsection{Ownership Rules} |
| |
| Whenever an object reference is passed into or out of a function, it |
| is part of the function's interface specification whether ownership is |
| transferred with the reference or not. |
| |
| Most functions that return a reference to an object pass on ownership |
| with the reference. In particular, all functions whose function it is |
| to create a new object, e.g.\ \code{PyInt_FromLong()} and |
| \code{Py_BuildValue()}, pass ownership to the receiver. Even if in |
| fact, in some cases, you don't receive a reference to a brand new |
| object, you still receive ownership of the reference. For instance, |
| \code{PyInt_FromLong()} maintains a cache of popular values and can |
| return a reference to a cached item. |
| |
| Many functions that extract objects from other objects also transfer |
| ownership with the reference, for instance |
| \code{PyObject_GetAttrString()}. The picture is less clear, here, |
| however, since a few common routines are exceptions: |
| \code{PyTuple_GetItem()}, \code{PyList_GetItem()} and |
| \code{PyDict_GetItem()} (and \code{PyDict_GetItemString()}) all return |
| references that you borrow from the tuple, list or dictionary. |
| |
| The function \code{PyImport_AddModule()} also returns a borrowed |
| reference, even though it may actually create the object it returns: |
| this is possible because an owned reference to the object is stored in |
| \code{sys.modules}. |
| |
| When you pass an object reference into another function, in general, |
| the function borrows the reference from you --- if it needs to store |
| it, it will use \code{Py_INCREF()} to become an independent owner. |
| There are exactly two important exceptions to this rule: |
| \code{PyTuple_SetItem()} and \code{PyList_SetItem()}. These functions |
| take over ownership of the item passed to them --- even if they fail! |
| (Note that \code{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 |
| \code{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} |
| |
| 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 |
| \code{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 \code{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 \code{__del__()} method. If this |
| class instance has a reference count of 1, disposing of it will call |
| its \code{__del__()} method. |
| |
| Since it is written in Python, the \code{__del__()} method can execute |
| arbitrary Python code. Could it perhaps do something to invalidate |
| the reference to \code{item} in \code{bug()}? You bet! Assuming that |
| the list passed into \code{bug()} is accessible to the |
| \code{__del__()} method, it could execute a statement to the effect of |
| \code{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 \code{__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} |
| |
| In general, functions that take object references as arguments don't |
| 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 slower. |
| |
| 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 |
| \code{malloc()} or from a function that may raise an exception. |
| |
| The macros \code{Py_INCREF()} and \code{Py_DECREF()} |
| don't check for \NULL{} pointers --- however, their variants |
| \code{Py_XINCREF()} and \code{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. |
| |
| |
| \section{Writing Extensions in \Cpp{}} |
| |
| 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). |
| |
| \chapter{Embedding Python in another application} |
| |
| 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 \code{Py_Initialize()}. 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 \code{PyRun_SimpleString()}, |
| or you can pass a stdio file pointer and a file name (for |
| identification in error messages only) to \code{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}. |
| |
| |
| \section{Embedding Python in \Cpp{}} |
| |
| 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{}. |
| |
| |
| \chapter{Dynamic Loading} |
| |
| On most modern systems it is possible to configure Python to support |
| dynamic loading of extension modules implemented in \C{}. When shared |
| libraries are used dynamic loading is configured automatically; |
| otherwise you have to select it as a build option (see below). Once |
| configured, dynamic loading is trivial to use: when a Python program |
| executes \code{import spam}, the search for modules tries to find a |
| file \file{spammodule.o} (\file{spammodule.so} when using shared |
| libraries) in the module search path, and if one is found, it is |
| loaded into the executing binary and executed. Once loaded, the |
| module acts just like a built-in extension module. |
| |
| The advantages of dynamic loading are twofold: the ``core'' Python |
| binary gets smaller, and users can extend Python with their own |
| modules implemented in \C{} without having to build and maintain their |
| own copy of the Python interpreter. There are also disadvantages: |
| dynamic loading isn't available on all systems (this just means that |
| on some systems you have to use static loading), and dynamically |
| loading a module that was compiled for a different version of Python |
| (e.g. with a different representation of objects) may dump core. |
| |
| |
| \section{Configuring and Building the Interpreter for Dynamic Loading} |
| |
| There are three styles of dynamic loading: one using shared libraries, |
| one using SGI IRIX 4 dynamic loading, and one using GNU dynamic |
| loading. |
| |
| \subsection{Shared Libraries} |
| |
| The following systems support dynamic loading using shared libraries: |
| SunOS 4; Solaris 2; SGI IRIX 5 (but not SGI IRIX 4!); and probably all |
| systems derived from SVR4, or at least those SVR4 derivatives that |
| support shared libraries (are there any that don't?). |
| |
| You don't need to do anything to configure dynamic loading on these |
| systems --- the \file{configure} detects the presence of the |
| \file{<dlfcn.h>} header file and automatically configures dynamic |
| loading. |
| |
| \subsection{SGI IRIX 4 Dynamic Loading} |
| |
| Only SGI IRIX 4 supports dynamic loading of modules using SGI dynamic |
| loading. (SGI IRIX 5 might also support it but it is inferior to |
| using shared libraries so there is no reason to; a small test didn't |
| work right away so I gave up trying to support it.) |
| |
| Before you build Python, you first need to fetch and build the \code{dl} |
| package written by Jack Jansen. This is available by anonymous ftp |
| from \url{ftp://ftp.cwi.nl/pub/dynload}, file |
| \file{dl-1.6.tar.Z}. (The version number may change.) Follow the |
| instructions in the package's \file{README} file to build it. |
| |
| Once you have built \code{dl}, you can configure Python to use it. To |
| this end, you run the \file{configure} script with the option |
| \code{--with-dl=\var{directory}} where \var{directory} is the absolute |
| pathname of the \code{dl} directory. |
| |
| Now build and install Python as you normally would (see the |
| \file{README} file in the toplevel Python directory.) |
| |
| \subsection{GNU Dynamic Loading} |
| |
| GNU dynamic loading supports (according to its \file{README} file) the |
| following hardware and software combinations: VAX (Ultrix), Sun 3 |
| (SunOS 3.4 and 4.0), Sparc (SunOS 4.0), Sequent Symmetry (Dynix), and |
| Atari ST. There is no reason to use it on a Sparc; I haven't seen a |
| Sun 3 for years so I don't know if these have shared libraries or not. |
| |
| You need to fetch and build two packages. |
| One is GNU DLD. All development of this code has been done with DLD |
| version 3.2.3, which is available by anonymous ftp from |
| \url{ftp://ftp.cwi.nl/pub/dynload}, file |
| \file{dld-3.2.3.tar.Z}. (A more recent version of DLD is available |
| via \url{http://www-swiss.ai.mit.edu/~jaffer/DLD.html} but this has |
| not been tested.) |
| The other package needed is an |
| emulation of Jack Jansen's \code{dl} package that I wrote on top of |
| GNU DLD 3.2.3. This is available from the same host and directory, |
| file \file{dl-dld-1.1.tar.Z}. (The version number may change --- but I doubt |
| it will.) Follow the instructions in each package's \file{README} |
| file to configure and build them. |
| |
| Now configure Python. Run the \file{configure} script with the option |
| \code{--with-dl-dld=\var{dl-directory},\var{dld-directory}} where |
| \var{dl-directory} is the absolute pathname of the directory where you |
| have built the \file{dl-dld} package, and \var{dld-directory} is that |
| of the GNU DLD package. The Python interpreter you build hereafter |
| will support GNU dynamic loading. |
| |
| |
| \section{Building a Dynamically Loadable Module} |
| |
| Since there are three styles of dynamic loading, there are also three |
| groups of instructions for building a dynamically loadable module. |
| Instructions common for all three styles are given first. Assuming |
| your module is called \code{spam}, the source filename must be |
| \file{spammodule.c}, so the object name is \file{spammodule.o}. The |
| module must be written as a normal Python extension module (as |
| described earlier). |
| |
| Note that in all cases you will have to create your own Makefile that |
| compiles your module file(s). This Makefile will have to pass two |
| \samp{-I} arguments to the \C{} compiler which will make it find the |
| Python header files. If the Make variable \var{PYTHONTOP} points to |
| the toplevel Python directory, your \var{CFLAGS} Make variable should |
| contain the options \samp{-I\$(PYTHONTOP) -I\$(PYTHONTOP)/Include}. |
| (Most header files are in the \file{Include} subdirectory, but the |
| \file{config.h} header lives in the toplevel directory.) |
| |
| |
| \subsection{Shared Libraries} |
| |
| You must link the \file{.o} file to produce a shared library. This is |
| done using a special invocation of the \UNIX{} loader/linker, |
| \emph{ld}(1). Unfortunately the invocation differs slightly per |
| system. |
| |
| On SunOS 4, use |
| \begin{verbatim} |
| ld spammodule.o -o spammodule.so |
| \end{verbatim} |
| |
| On Solaris 2, use |
| \begin{verbatim} |
| ld -G spammodule.o -o spammodule.so |
| \end{verbatim} |
| |
| On SGI IRIX 5, use |
| \begin{verbatim} |
| ld -shared spammodule.o -o spammodule.so |
| \end{verbatim} |
| |
| On other systems, consult the manual page for \code{ld}(1) to find what |
| flags, if any, must be used. |
| |
| If your extension module uses system libraries that haven't already |
| been linked with Python (e.g. a windowing system), these must be |
| passed to the \code{ld} command as \samp{-l} options after the |
| \samp{.o} file. |
| |
| The resulting file \file{spammodule.so} must be copied into a directory |
| along the Python module search path. |
| |
| |
| \subsection{SGI IRIX 4 Dynamic Loading} |
| |
| \strong{IMPORTANT:} You must compile your extension module with the |
| additional \C{} flag \samp{-G0} (or \samp{-G 0}). This instruct the |
| assembler to generate position-independent code. |
| |
| You don't need to link the resulting \file{spammodule.o} file; just |
| copy it into a directory along the Python module search path. |
| |
| The first time your extension is loaded, it takes some extra time and |
| a few messages may be printed. This creates a file |
| \file{spammodule.ld} which is an image that can be loaded quickly into |
| the Python interpreter process. When a new Python interpreter is |
| installed, the \code{dl} package detects this and rebuilds |
| \file{spammodule.ld}. The file \file{spammodule.ld} is placed in the |
| directory where \file{spammodule.o} was found, unless this directory is |
| unwritable; in that case it is placed in a temporary |
| directory.\footnote{Check the manual page of the \code{dl} package for |
| details.} |
| |
| If your extension modules uses additional system libraries, you must |
| create a file \file{spammodule.libs} in the same directory as the |
| \file{spammodule.o}. This file should contain one or more lines with |
| whitespace-separated options that will be passed to the linker --- |
| normally only \samp{-l} options or absolute pathnames of libraries |
| (\samp{.a} files) should be used. |
| |
| |
| \subsection{GNU Dynamic Loading} |
| |
| Just copy \file{spammodule.o} into a directory along the Python module |
| search path. |
| |
| If your extension modules uses additional system libraries, you must |
| create a file \file{spammodule.libs} in the same directory as the |
| \file{spammodule.o}. This file should contain one or more lines with |
| whitespace-separated absolute pathnames of libraries (\samp{.a} |
| files). No \samp{-l} options can be used. |
| |
| |
| %\input{extref} |
| |
| \input{ext.ind} |
| |
| \end{document} |