Wprowadzenie

Interfejs programowania aplikacji w Pythonie daje programistom języków C i C++ dostęp do programu interpretującego polecenia języka pytonowskiego na wielu poziomach. Sprzęg (API) jest równo użyteczny z poziomu C++ ale dla porządku jest zwykle określany mianem sprzęgu pomiędzy językami pytonowskim a C (z ang. - Python/C API). Istnieją dwie zasadniczo różne przyczyny dla użycia interfejsu między językami Python i C. Pierwszą przyczyną jest pisanie modułów rozszerzających dla szczególnych powodów; są to moduły języka C, które rozszerzają interpreter Pythona. To jest zwykle najczęstsze użycie. Drugą przyczyną jest użycie Pythona jako komponentu większego programu; ta technika jest zwykle określana mianem załączania - z ang. - embedding w aplikacji.

Writing an extension module is a relatively well-understood process, where a „cookbook” approach works well. There are several tools that automate the process to some extent. While people have embedded Python in other applications since its early existence, the process of embedding Python is less straightforward than writing an extension.

Wiele zadań sprzęgu (API) jest użytecznych niezależnie od tego czy załączasz, czy też rozszerzasz program interpretujący język pytonowski; co więcej, większość aplikacji które załącza program interpretujący polecenia jezyka pytonowskiego potrzebuje także szczególnych rozszerzeń, więc prawdopodobnie jest dobrym pomysłem zaznajomienie się z pisaniem rozszerzenia przed próbą załączenia języka pytonowskiego w prawdziwej aplikacji.

Coding standards

If you’re writing C code for inclusion in CPython, you must follow the guidelines and standards defined in PEP 7. These guidelines apply regardless of the version of Python you are contributing to. Following these conventions is not necessary for your own third party extension modules, unless you eventually expect to contribute them to Python.

Pliki Włączania - z ang. Include

Wszystkie zadania, definicje typu i makropoleceń konieczne do użycia sprzęgu między językami pytonowskim i C są włączane do źródeł w kodzie użytkownika przez następującą linijkę:

#define PY_SSIZE_T_CLEAN
#include <Python.h>

This implies inclusion of the following standard headers: <stdio.h>, <string.h>, <errno.h>, <limits.h>, <assert.h> and <stdlib.h> (if available).

Informacja

Jako że Python może definiować pewne definicje preprocesora, które wpływają na pliki nagłówkowe na niektórych systemach, musisz załączyć plik Python.h przed jakimikolwiek standardowymi nagłówkami.

It is recommended to always define PY_SSIZE_T_CLEAN before including Python.h. See Pobieranie kolejnych rzeczy podanych na wejściu i konstruowanie wartości. for a description of this macro.

Wszystkie widoczne dla użytkownika nazwy określone w Python.h ( z wyjątkiem tych określonych przez załączone standardowe pliki nagłówkowe ) mają jeden z przedrostków Py lub _Py. Nazwy rozpoczynające się od _Py służą do wewnętrznego użytku przez urzeczywistnienie programu interpretującego języka pytonowskiego i nie powinno być używane przez piszących rozszerzenia. Nazwy członków struktury nie mają zarezerwowanych przedrostków.

Informacja

User code should never define names that begin with Py or _Py. This confuses the reader, and jeopardizes the portability of the user code to future Python versions, which may define additional names beginning with one of these prefixes.

The header files are typically installed with Python. On Unix, these are located in the directories prefix/include/pythonversion/ and exec_prefix/include/pythonversion/, where prefix and exec_prefix are defined by the corresponding parameters to Python’s configure script and version is '%d.%d' % sys.version_info[:2]. On Windows, the headers are installed in prefix/include, where prefix is the installation directory specified to the installer.

To include the headers, place both directories (if different) on your compiler’s search path for includes. Do not place the parent directories on the search path and then use #include <pythonX.Y/Python.h>; this will break on multi-platform builds since the platform independent headers under prefix include the platform specific headers from exec_prefix.

C++ users should note that although the API is defined entirely using C, the header files properly declare the entry points to be extern "C". As a result, there is no need to do anything special to use the API from C++.

Useful macros

Several useful macros are defined in the Python header files. Many are defined closer to where they are useful (e.g. Py_RETURN_NONE). Others of a more general utility are defined here. This is not necessarily a complete listing.

Py_UNREACHABLE()

Use this when you have a code path that cannot be reached by design. For example, in the default: clause in a switch statement for which all possible values are covered in case statements. Use this in places where you might be tempted to put an assert(0) or abort() call.

In release mode, the macro helps the compiler to optimize the code, and avoids a warning about unreachable code. For example, the macro is implemented with __builtin_unreachable() on GCC in release mode.

A use for Py_UNREACHABLE() is following a call a function that never returns but that is not declared _Py_NO_RETURN.

If a code path is very unlikely code but can be reached under exceptional case, this macro must not be used. For example, under low memory condition or if a system call returns a value out of the expected range. In this case, it’s better to report the error to the caller. If the error cannot be reported to caller, Py_FatalError() can be used.

Nowe w wersji 3.7.

Py_ABS(x)

Return the absolute value of x.

Nowe w wersji 3.3.

Py_MIN(x, y)

Return the minimum value between x and y.

Nowe w wersji 3.3.

Py_MAX(x, y)

Return the maximum value between x and y.

Nowe w wersji 3.3.

Py_STRINGIFY(x)

Convert x to a C string. E.g. Py_STRINGIFY(123) returns "123".

Nowe w wersji 3.4.

Py_MEMBER_SIZE(type, member)

Return the size of a structure (type) member in bytes.

Nowe w wersji 3.6.

Py_CHARMASK(c)

Argument must be a character or an integer in the range [-128, 127] or [0, 255]. This macro returns c cast to an unsigned char.

Py_GETENV(s)

Like getenv(s), but returns NULL if -E was passed on the command line (i.e. if Py_IgnoreEnvironmentFlag is set).

Py_UNUSED(arg)

Use this for unused arguments in a function definition to silence compiler warnings. Example: int func(int a, int Py_UNUSED(b)) { return a; }.

Nowe w wersji 3.4.

Py_DEPRECATED(version)

Use this for deprecated declarations. The macro must be placed before the symbol name.

Example:

Py_DEPRECATED(3.8) PyAPI_FUNC(int) Py_OldFunction(void);

Zmienione w wersji 3.8: MSVC support was added.

PyDoc_STRVAR(name, str)

Creates a variable with name name that can be used in docstrings. If Python is built without docstrings, the value will be empty.

Use PyDoc_STRVAR for docstrings to support building Python without docstrings, as specified in PEP 7.

Example:

PyDoc_STRVAR(pop_doc, "Remove and return the rightmost element.");

static PyMethodDef deque_methods[] = {
    // ...
    {"pop", (PyCFunction)deque_pop, METH_NOARGS, pop_doc},
    // ...
}

PyDoc_STR(str)

Creates a docstring for the given input string or an empty string if docstrings are disabled.

Use PyDoc_STR in specifying docstrings to support building Python without docstrings, as specified in PEP 7.

Example:

static PyMethodDef pysqlite_row_methods[] = {
    {"keys", (PyCFunction)pysqlite_row_keys, METH_NOARGS,
        PyDoc_STR("Returns the keys of the row.")},
    {NULL, NULL}
};

Przedmioty, ich Rodzaje i Liczby Odwołań

Most Python/C API functions have one or more arguments as well as a return value of type PyObject*. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type PyObject, only pointer variables of type PyObject* can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static PyTypeObject objects.

Wszystkie przedmioty języka pytonowskiego (nawet liczby całkowite języka pytonowskiego) mają rodzaj i liczbę odniesień. Typ przedmiotu określa jakiego rodzaju przedmiot to jest (np. liczba całkowita, lista, lub zadanie zdefiniowane przez użytkownika; jest wiele więcej jak wyjaśniono w The standard type hierarchy). Dla każdego z dobrze-znanych rodzajów istnieje makropolecenie sprawdzające czy przedmiot jest tego rodzaju; na przykład, PyList_Check(a) jest prawdziwe wtedy (i tylko wtedy) gdy przedmiot na który wskazuje a jest lista z języka pytonowskiego.

Liczby odniesień

The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a strong reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When the last strong reference to an object is released (i.e. its reference count becomes zero), the object is deallocated. If it contains references to other objects, those references are released. Those other objects may be deallocated in turn, if there are no more references to them, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is „don’t do that.”)

Reference counts are always manipulated explicitly. The normal way is to use the macro Py_INCREF() to take a new reference to an object (i.e. increment its reference count by one), and Py_DECREF() to release that reference (i.e. decrement the reference count by one). The Py_DECREF() macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object’s deallocator to be called. The deallocator is a function pointer contained in the object’s type structure. The type-specific deallocator takes care of releasing references for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that’s needed. There’s no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming sizeof(Py_ssize_t) >= sizeof(void*)). Thus, the reference count increment is a simple operation.

It is not necessary to hold a strong reference (i.e. increment the reference count) for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to take a new strong reference (i.e. increment the reference count) temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.

However, a common pitfall is to extract an object from a list and hold on to it for a while without taking a new reference. Some other operation might conceivably remove the object from the list, releasing that reference, and possibly deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a Py_DECREF(), so almost any operation is potentially dangerous.

A safe approach is to always use the generic operations (functions whose name begins with PyObject_, PyNumber_, PySequence_ or PyMapping_). These operations always create a new strong reference (i.e. increment the reference count) of the object they return. This leaves the caller with the responsibility to call Py_DECREF() when they are done with the result; this soon becomes second nature.

Szczegóły Liczby Odniesień

The reference count behavior of functions in the Python/C API is best explained in terms of ownership of references. Ownership pertains to references, never to objects (objects are not owned: they are always shared). „Owning a reference” means being responsible for calling Py_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually releasing it by calling Py_DECREF() or Py_XDECREF() when it’s no longer needed—or passing on this responsibility (usually to its caller). When a function passes ownership of a reference on to its caller, the caller is said to receive a new reference. When no ownership is transferred, the caller is said to borrow the reference. Nothing needs to be done for a borrowed reference.

Idąc dalej, gdy wywołujące zadanie przekazuje odniesienie do przedmiotu, istnieją dwie możliwości: zadanie kradnie odniesienie do przedmiotu, lub nie kradnie go. Kradnięcie odniesienia oznacza, że gdy przekazujesz odniesienie do zadania, to zadanie przyjmuje, że teraz ono posiada odniesienie i nie jesteś za nie odpowiedzialny ani chwili dłużej.

Few functions steal references; the two notable exceptions are PyList_SetItem() and PyTuple_SetItem(), which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple (1, 2, "three") could look like this (forgetting about error handling for the moment; a better way to code this is shown below):

PyObject *t;

t = PyTuple_New(3);
PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));

Here, PyLong_FromLong() returns a new reference which is immediately stolen by PyTuple_SetItem(). When you want to keep using an object although the reference to it will be stolen, use Py_INCREF() to grab another reference before calling the reference-stealing function.

Incidentally, PyTuple_SetItem() is the only way to set tuple items; PySequence_SetItem() and PyObject_SetItem() refuse to do this since tuples are an immutable data type. You should only use PyTuple_SetItem() for tuples that you are creating yourself.

Equivalent code for populating a list can be written using PyList_New() and PyList_SetItem().

However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function, Py_BuildValue(), that can create most common objects from C values, directed by a format string. For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking):

PyObject *tuple, *list;

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

It is much more common to use PyObject_SetItem() and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding references is much saner, since you don’t have to take a new reference just so you can give that reference away („have it be stolen”). For example, this function sets all items of a list (actually, any mutable sequence) to a given item:

int
set_all(PyObject *target, PyObject *item)
{
    Py_ssize_t i, n;

    n = PyObject_Length(target);
    if (n < 0)
        return -1;
    for (i = 0; i < n; i++) {
        PyObject *index = PyLong_FromSsize_t(i);
        if (!index)
            return -1;
        if (PyObject_SetItem(target, index, item) < 0) {
            Py_DECREF(index);
            return -1;
        }
        Py_DECREF(index);
    }
    return 0;
}

The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a reference to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like PyObject_GetItem() and PySequence_GetItem(), always return a new reference (the caller becomes the owner of the reference).

It is important to realize that whether you own a reference returned by a function depends on which function you call only — the plumage (the type of the object passed as an argument to the function) doesn’t enter into it! Thus, if you extract an item from a list using PyList_GetItem(), you don’t own the reference — but if you obtain the same item from the same list using PySequence_GetItem() (which happens to take exactly the same arguments), you do own a reference to the returned object.

Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using PyList_GetItem(), and once using PySequence_GetItem().

long
sum_list(PyObject *list)
{
    Py_ssize_t i, n;
    long total = 0, value;
    PyObject *item;

    n = PyList_Size(list);
    if (n < 0)
        return -1; /* Not a list */
    for (i = 0; i < n; i++) {
        item = PyList_GetItem(list, i); /* Can't fail */
        if (!PyLong_Check(item)) continue; /* Skip non-integers */
        value = PyLong_AsLong(item);
        if (value == -1 && PyErr_Occurred())
            /* Integer too big to fit in a C long, bail out */
            return -1;
        total += value;
    }
    return total;
}

long
sum_sequence(PyObject *sequence)
{
    Py_ssize_t i, n;
    long total = 0, value;
    PyObject *item;
    n = PySequence_Length(sequence);
    if (n < 0)
        return -1; /* Has no length */
    for (i = 0; i < n; i++) {
        item = PySequence_GetItem(sequence, i);
        if (item == NULL)
            return -1; /* Not a sequence, or other failure */
        if (PyLong_Check(item)) {
            value = PyLong_AsLong(item);
            Py_DECREF(item);
            if (value == -1 && PyErr_Occurred())
                /* Integer too big to fit in a C long, bail out */
                return -1;
            total += value;
        }
        else {
            Py_DECREF(item); /* Discard reference ownership */
        }
    }
    return total;
}

Typy

There are few other data types that play a significant role in the Python/C API; most are simple C types such as int, long, double and char*. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.

type Py_ssize_t

Part of the Stable ABI.

A signed integral type such that sizeof(Py_ssize_t) == sizeof(size_t). C99 doesn’t define such a thing directly (size_t is an unsigned integral type). See PEP 353 for details. PY_SSIZE_T_MAX is the largest positive value of type Py_ssize_t.

Wyjątki

Programujący komputer w języku pytonowskim musi sobie zaprzątać głowę tylko sytuacjami wyjątkowymi tylko jeśli szczególna obsługa błędów jest konieczna; Nieobsłużone wyjątki są automatycznie przesyłane do zadania wywołującego, potem do zadania które wywołało tamto zadanie, i tak dalej, dopóki nie natrafi na program interpretujący najwyższego poziomu, gdzie są przekazywane użytkownikowi wraz z wypisem kolejnych wywołań odłożonych na stercie.

For C programmers, however, error checking always has to be explicit. All functions in the Python/C API can raise exceptions, unless an explicit claim is made otherwise in a function’s documentation. In general, when a function encounters an error, it sets an exception, discards any object references that it owns, and returns an error indicator. If not documented otherwise, this indicator is either NULL or -1, depending on the function’s return type. A few functions return a Boolean true/false result, with false indicating an error. Very few functions return no explicit error indicator or have an ambiguous return value, and require explicit testing for errors with PyErr_Occurred(). These exceptions are always explicitly documented.

Exception state is maintained in per-thread storage (this is equivalent to using global storage in an unthreaded application). A thread can be in one of two states: an exception has occurred, or not. The function PyErr_Occurred() can be used to check for this: it returns a borrowed reference to the exception type object when an exception has occurred, and NULL otherwise. There are a number of functions to set the exception state: PyErr_SetString() is the most common (though not the most general) function to set the exception state, and PyErr_Clear() clears the exception state.

The full exception state consists of three objects (all of which can be NULL): the exception type, the corresponding exception value, and the traceback. These have the same meanings as the Python result of sys.exc_info(); however, they are not the same: the Python objects represent the last exception being handled by a Python try … except statement, while the C level exception state only exists while an exception is being passed on between C functions until it reaches the Python bytecode interpreter’s main loop, which takes care of transferring it to sys.exc_info() and friends.

Zauważ że poczynając od języka pytonowskiego w wersji 1.5 preferowaną, bezpiecznym dla wątków sposobem na dostęp do stanu wyjątku z poziomu kodu napisanego w języku pytonowskim jest wezwanie zadania sys.exc_info(), które zwraca określony-dla-wątku stan wyjątku dla kodu napisanego w języku pytonowskim. Poza tym składnia obu sposobów na dostęp do stanu sytuacji wyjątkowej zmieniła się tak, że zadanie które złapie wyjątek zachowa i przywróci swój stan wyjątku tak, aby zachować stan wyjątku wywołujacego zadanie. To działanie zapobiega typowym błędom w obsłudze sytuacji wyjątkowych powodowanych przez niewinnie-wyglądające zadania nadpisujące sytuacje wyjątkowe które aktualnie są obsługiwane; to także redukuje często niechciane wydłużanie czasu życia przedmiotów do których odnosi się ramka stosu w wypisie śladu wywołań.

Jako nadrzędną zasadę, przyjmuje się że zadanie które wywołuje inne zadanie do wykonania pewnych operacji powinno sprawdzić czy wywołane zadanie zgłosiło wyjątek, a jeśli tak, to przekazać stan wyjątku do wywołującego. Powinno też odrzucić jakiekolwiek odniesienia do przedmiotów, które posiada, i zwrócić sygnalizator błędu, ale nie powinno ustawiać innego wyjątku — który nadpisywałby wyjątek, który właśnie został zgłoszony i tracić istotne informacje o dokładnym powodzie błędu.

A simple example of detecting exceptions and passing them on is shown in the sum_sequence() example above. It so happens that this example doesn’t need to clean up any owned references when it detects an error. The following example function shows some error cleanup. First, to remind you why you like Python, we show the equivalent Python code:

def incr_item(dict, key):
    try:
        item = dict[key]
    except KeyError:
        item = 0
    dict[key] = item + 1

Tu następuje odpowiadający kod w języku C, w całej pełni okazałości:

int
incr_item(PyObject *dict, PyObject *key)
{
    /* Objects all initialized to NULL for Py_XDECREF */
    PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;
    int rv = -1; /* Return value initialized to -1 (failure) */

    item = PyObject_GetItem(dict, key);
    if (item == NULL) {
        /* Handle KeyError only: */
        if (!PyErr_ExceptionMatches(PyExc_KeyError))
            goto error;

        /* Clear the error and use zero: */
        PyErr_Clear();
        item = PyLong_FromLong(0L);
        if (item == NULL)
            goto error;
    }
    const_one = PyLong_FromLong(1L);
    if (const_one == NULL)
        goto error;

    incremented_item = PyNumber_Add(item, const_one);
    if (incremented_item == NULL)
        goto error;

    if (PyObject_SetItem(dict, key, incremented_item) < 0)
        goto error;
    rv = 0; /* Success */
    /* Continue with cleanup code */

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

    /* Use Py_XDECREF() to ignore NULL references */
    Py_XDECREF(item);
    Py_XDECREF(const_one);
    Py_XDECREF(incremented_item);

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

This example represents an endorsed use of the goto statement in C! It illustrates the use of PyErr_ExceptionMatches() and PyErr_Clear() to handle specific exceptions, and the use of Py_XDECREF() to dispose of owned references that may be NULL (note the 'X' in the name; Py_DECREF() would crash when confronted with a NULL reference). It is important that the variables used to hold owned references are initialized to NULL for this to work; likewise, the proposed return value is initialized to -1 (failure) and only set to success after the final call made is successful.

Załączanie programu interpretującego język pytonowski

Jedno istotne zadanie, o które załączający (w przeciwieństwie do piszących rozszerzenia) program interpretujący język pytonowski muszą się martwić jest zainicjowanie i prawdopodobne zakończenie programu interpretującego polecenia języka pytonowskiego. Większość użyteczności programu interpretującego polecenia języka pytonowskiego może tylko być użyta po jego zainicjowaniu.

The basic initialization function is Py_Initialize(). This initializes the table of loaded modules, and creates the fundamental modules builtins, __main__, and sys. It also initializes the module search path (sys.path).

Py_Initialize() does not set the „script argument list” (sys.argv). If this variable is needed by Python code that will be executed later, it must be set explicitly with a call to PySys_SetArgvEx(argc, argv, updatepath) after the call to Py_Initialize().

On most systems (in particular, on Unix and Windows, although the details are slightly different), Py_Initialize() calculates the module search path based upon its best guess for the location of the standard Python interpreter executable, assuming that the Python library is found in a fixed location relative to the Python interpreter executable. In particular, it looks for a directory named lib/pythonX.Y relative to the parent directory where the executable named python is found on the shell command search path (the environment variable PATH).

Na przykład, jeśli plik wykonywalny programu interpretującego polecenia języka pytonowskiego znajduje się w katalogu /usr/local/bin/python, będzie zakładał, że biblioteki są w katalogu /usr/local/lib/pythonX.Y (Faktycznie, ta szczególna ścieżka jest także „ratunkowym” położeniem, używanym gdy żaden plik wykonywalny nazwany python nie znajdzie się w katalogach znajdujących się w zmiennej środowiskowej PATH.) Użytkownik może podmienić to zachowanie przez ustawienie zmiennej środowiskowej PYTHONHOME, lub wstawić dodatkowe katalogi przed sztandarową ścieżką przez ustawienie zmiennej środowiskowej PYTHONPATH.

The embedding application can steer the search by calling Py_SetProgramName(file) before calling Py_Initialize(). Note that PYTHONHOME still overrides this and PYTHONPATH is still inserted in front of the standard path. An application that requires total control has to provide its own implementation of Py_GetPath(), Py_GetPrefix(), Py_GetExecPrefix(), and Py_GetProgramFullPath() (all defined in Modules/getpath.c).

Sometimes, it is desirable to „uninitialize” Python. For instance, the application may want to start over (make another call to Py_Initialize()) or the application is simply done with its use of Python and wants to free memory allocated by Python. This can be accomplished by calling Py_FinalizeEx(). The function Py_IsInitialized() returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter. Notice that Py_FinalizeEx() does not free all memory allocated by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.

Odpluskwiające Budowy

Program interpretujący język pytonowski może być zbudowany z kilkoma makropoleceniami do załączenia dodatkowych sprawdzeń programu interpretującego polecenia języka pytonowskiego i modułów rozszerzających. Te sprawdzenia mają zwyczaj dodawać duży narzut czasu wykonania poleceń programu więc nie są załączane domyślnie.

A full list of the various types of debugging builds is in the file Misc/SpecialBuilds.txt in the Python source distribution. Builds are available that support tracing of reference counts, debugging the memory allocator, or low-level profiling of the main interpreter loop. Only the most frequently used builds will be described in the remainder of this section.

Compiling the interpreter with the Py_DEBUG macro defined produces what is generally meant by a debug build of Python. Py_DEBUG is enabled in the Unix build by adding --with-pydebug to the ./configure command. It is also implied by the presence of the not-Python-specific _DEBUG macro. When Py_DEBUG is enabled in the Unix build, compiler optimization is disabled.

In addition to the reference count debugging described below, extra checks are performed, see Python Debug Build.

Defining Py_TRACE_REFS enables reference tracing (see the configure --with-trace-refs option). When defined, a circular doubly linked list of active objects is maintained by adding two extra fields to every PyObject. Total allocations are tracked as well. Upon exit, all existing references are printed. (In interactive mode this happens after every statement run by the interpreter.)

Odwołaj się do Misc/SpecialBuilds.txt w źródłowym pakiecie języka pytonowskiego po więcej szczegółów.