# Using the C API: Assorted topics

The tutorial walked you through creating a C API extension module, but left many areas unexplained. This document looks at several concepts that you’ll need to learn in order to write more complex extensions.

## Błędy i wyjątki

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 `-1` or a `NULL` pointer). Exception information is stored in three members of the interpreter’s thread state. These are `NULL` if there is no exception. Otherwise they are the C equivalents of the members of the Python tuple returned by `sys.exc_info()`. These are the exception type, exception instance, and a traceback object. It is important to know about them to understand how errors are passed around.

Sprzęg języka pytonowskiego określa pewien zestaw zadań do ustawiania różnych rodzajów wyjątków.

The most common one is `PyErr_SetString()`. Its arguments are an exception object and a C string. The exception object is usually a predefined object like `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 `PyErr_SetFromErrno()`, which only takes an exception argument and constructs the associated value by inspection of the global variable `errno`. The most general function is `PyErr_SetObject()`, which takes two object arguments, the exception and its associated value. You don’t need to `Py_INCREF()` the objects passed to any of these functions.

You can test non-destructively whether an exception has been set with `PyErr_Occurred()`. This returns the current exception object, or `NULL` if no exception has occurred. You normally don’t need to call `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 f that calls another function g detects that the latter fails, f should itself return an error value (usually `NULL` or `-1`). It should not call one of the `PyErr_*` functions — one has already been called by g. f’s caller is then supposed to also return an error indication to its caller, again without calling `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 `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 `PyErr_Clear()`. The only time C code should call `PyErr_Clear()` is if it doesn’t want to pass the error on to the interpreter but wants to handle it completely by itself (possibly by trying something else, or pretending nothing went wrong).

Every failing `malloc()` call must be turned into an exception — the direct caller of `malloc()` (or `realloc()`) must call `PyErr_NoMemory()` and return a failure indicator itself. All the object-creating functions (for example, `PyLong_FromLong()`) already do this, so this note is only relevant to those who call `malloc()` directly.

Also note that, with the important exception of `PyArg_ParseTuple()` and friends, functions that return an integer status usually return a positive value or zero for success and `-1` for failure, like Unix system calls.

Finally, be careful to clean up garbage (by making `Py_XDECREF()` or `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, such as `PyExc_ZeroDivisionError`, which you can use directly. Of course, you should choose exceptions wisely — don’t use `PyExc_TypeError` to mean that a file couldn’t be opened (that should probably be `PyExc_OSError`). If something’s wrong with the argument list, the `PyArg_ParseTuple()` function usually raises `PyExc_TypeError`. If you have an argument whose value must be in a particular range or must satisfy other conditions, `PyExc_ValueError` is appropriate.

You can also define a new exception that is unique to your module. The simplest way to do this is to declare a static global object variable at the beginning of the file:

    static PyObject *SpamError = NULL;

and initialize it by calling `PyErr_NewException()` in the module’s `Py_mod_exec` function (`spam_module_exec()`):

    SpamError = PyErr_NewException("spam.error", NULL, NULL);

Since `SpamError` is a global variable, it will be overwritten every time the module is reinitialized, when the `Py_mod_exec` function is called.

For now, let’s avoid the issue: we will block repeated initialization by raising an `ImportError`:

    static PyObject *SpamError = NULL;

    static int
    spam_module_exec(PyObject *m)
    {
        if (SpamError != NULL) {
            PyErr_SetString(PyExc_ImportError,
                            "cannot initialize spam module more than once");
            return -1;
        }
        SpamError = PyErr_NewException("spam.error", NULL, NULL);
        if (PyModule_AddObjectRef(m, "SpamError", SpamError) < 0) {
            return -1;
        }

        return 0;
    }

    static PyModuleDef_Slot spam_module_slots[] = {
        {Py_mod_exec, spam_module_exec},
        {0, NULL}
    };

    static struct PyModuleDef spam_module = {
        .m_base = PyModuleDef_HEAD_INIT,
        .m_name = "spam",
        .m_size = 0,  // non-negative
        .m_slots = spam_module_slots,
    };

    PyMODINIT_FUNC
    PyInit_spam(void)
    {
        return PyModuleDef_Init(&spam_module);
    }

Note that the Python name for the exception object is `spam.error`. The `PyErr_NewException()` function may create a class with the base class being `Exception` (unless another class is passed in instead of `NULL`), described in Built-in Exceptions.

Note also that the `SpamError` variable retains a reference to the newly created exception class; this is intentional! Since the exception could be removed from the module by external code, an owned reference to the class is needed to ensure that it will not be discarded, causing `SpamError` to become a dangling pointer. Should it become a dangling pointer, C code which raises the exception could cause a core dump or other unintended side effects.

For now, the `Py_DECREF()` call to remove this reference is missing. Even when the Python interpreter shuts down, the global `SpamError` variable will not be garbage-collected. It will „leak”. We did, however, ensure that this will happen at most once per process.

We discuss the use of `PyMODINIT_FUNC` as a function return type later in this sample.

The `spam.error` exception can be raised in your extension module using a call to `PyErr_SetString()` as shown below:

    static PyObject *
    spam_system(PyObject *self, PyObject *args)
    {
        const char *command;
        int sts;

        if (!PyArg_ParseTuple(args, "s", &command))
            return NULL;
        sts = system(command);
        if (sts < 0) {
            PyErr_SetString(SpamError, "System command failed");
            return NULL;
        }
        return PyLong_FromLong(sts);
    }

## Embedding an extension

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. On Unix, place your file (`spammodule.c` for example) in the `Modules/` directory of an unpacked source distribution, add a line to the file `Modules/Setup.local` describing your file:

    spam spammodule.o

i przebuduj program interpretujący przez uruchomienie programu make w katalogu głównym instalacji. Możesz także uruchomić program make w podkatalogu `Modules/`, ale wtedy musisz najpierw przebudować plik `Makefile` tam przez uruchomienie programu make Makefile». To jest konieczne za każdym razem gdy zmieniasz plik `Setup`.)

If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:

    spam spammodule.o -lX11

## Wywoływanie zadań języka pytonowskiego z C

The tutorial 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.

Szczęśliwie, program interpretujący polecenia języka pytonowskiego jest łatwo wywoływany rekursywnie i istnieje standardowy sprzęg aby wywołać zadanie języka pytonowskiego. (Nie będę rozpisywał się o tym jak wywołać czytnik języka pytonowskiego z konkretnym ciągiem znaków na wejściu — jeśli jesteś zainteresowany, spójrz na wypełnienie opcji `-c` wiersza polecenia w `Modules/main.c` z kodu źródłowego języka pytonowskiego.)

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 `Py_INCREF()` it!) in a global variable — or wherever you see fit. For example, the following function might be part of a module definition:

    static PyObject *my_callback = NULL;

    static PyObject *
    my_set_callback(PyObject *dummy, PyObject *args)
    {
        PyObject *result = NULL;
        PyObject *temp;

        if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
            if (!PyCallable_Check(temp)) {
                PyErr_SetString(PyExc_TypeError, "parameter must be callable");
                return NULL;
            }
            Py_XINCREF(temp);         /* Add a reference to new callback */
            Py_XDECREF(my_callback);  /* Dispose of previous callback */
            my_callback = temp;       /* Remember new callback */
            /* Boilerplate to return "None" */
            Py_INCREF(Py_None);
            result = Py_None;
        }
        return result;
    }

This function must be registered with the interpreter using the `METH_VARARGS` flag in `PyMethodDef.ml_flags`. The `PyArg_ParseTuple()` function and its arguments are documented in section Wydobywanie parametrów w zadaniach rozszerzających.

The macros `Py_XINCREF()` and `Py_XDECREF()` increment/decrement the reference count of an object and are safe in the presence of `NULL` pointers (but note that temp will not be `NULL` in this context). More info on them in section Liczby odniesień.

Later, when it is time to call the function, you call the C function `PyObject_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 in `NULL`, or an empty tuple; to call it with one argument, pass a singleton tuple. `Py_BuildValue()` returns a tuple when its format string consists of zero or more format codes between parentheses. For example:

    int arg;
    PyObject *arglist;
    PyObject *result;
    ...
    arg = 123;
    ...
    /* Time to call the callback */
    arglist = Py_BuildValue("(i)", arg);
    result = PyObject_CallObject(my_callback, arglist);
    Py_DECREF(arglist);

`PyObject_CallObject()` returns a Python object pointer: this is the return value of the Python function. `PyObject_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 `Py_DECREF()`-ed immediately after the `PyObject_CallObject()` call.

The return value of `PyObject_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 `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 `PyObject_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 `PyErr_Clear()`. For example:

    if (result == NULL)
        return NULL; /* Pass error back */
    ...use result...
    Py_DECREF(result);

Depending on the desired interface to the Python callback function, you may also have to provide an argument list to `PyObject_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 `Py_BuildValue()`. For example, if you want to pass an integral event code, you might use the following code:

    PyObject *arglist;
    ...
    arglist = Py_BuildValue("(l)", eventcode);
    result = PyObject_CallObject(my_callback, arglist);
    Py_DECREF(arglist);
    if (result == NULL)
        return NULL; /* Pass error back */
    /* Here maybe use the result */
    Py_DECREF(result);

Note the placement of `Py_DECREF(arglist)` immediately after the call, before the error check! Also note that strictly speaking this code is not complete: `Py_BuildValue()` may run out of memory, and this should be checked.

You may also call a function with keyword arguments by using `PyObject_Call()`, which supports arguments and keyword arguments. As in the above example, we use `Py_BuildValue()` to construct the dictionary.

    PyObject *dict;
    ...
    dict = Py_BuildValue("{s:i}", "name", val);
    result = PyObject_Call(my_callback, NULL, dict);
    Py_DECREF(dict);
    if (result == NULL)
        return NULL; /* Pass error back */
    /* Here maybe use the result */
    Py_DECREF(result);

## Wydobywanie parametrów w zadaniach rozszerzających

The tutorial uses a „`METH_O`” function, which is limited to a single Python argument. If you want more, you can use `METH_VARARGS` instead. With this flag, the C function will receive a `tuple` of arguments instead of a single object.

For unpacking the tuple, CPython provides the `PyArg_ParseTuple()` function, declared as follows:

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

Parametr arg musi być przedmiotem - krotką zawierającym listę parametrów z języka pytonowskiego dla zadania C. Parametr format musi być ciągiem formatu, którego składnia jest wyjaśniona w Pobieranie kolejnych rzeczy podanych na wejściu i konstruowanie wartości. w podręczniku użytkownika API Python/C. Pozostałe parametry muszą być adresami zmiennych których rodzaj jest określony przez ciąg formatujący.

For example, to receive a single Python `str` object and turn it into a C buffer, you would use `"s"` as the format string:

    const char *command;
    if (!PyArg_ParseTuple(args, "s", &command)) {
        return NULL;
    }

If an error is detected in the argument list, `PyArg_ParseTuple()` returns `NULL` (the error indicator for functions returning object pointers); your function may return `NULL`, relying on the exception set by `PyArg_ParseTuple()`.

Note that while `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!

Zauważ, że dowolne odniesienia do przedmiotów języka pytonowskiego, które są dostarczone wołającemu są pożyczonymi odniesieniami; nie zmniejszaj liczby tych odniesień.

Pewne przykładowe wywołania:

    #include <Python.h>

    int ok;
    int i, j;
    long k, l;
    const char *s;
    Py_ssize_t 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') */

    {
        const char *file;
        const 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) */
    }

## Parametry kluczowe dla zadań rozszerzających

If you also want your function to accept keyword arguments, use the `METH_KEYWORDS` flag in combination with `METH_VARARGS`. (`METH_KEYWORDS` can also be used with other flags; see its documentation for the allowed combinations.)

In this case, the C function should accept a third `PyObject *` parameter which will be a dictionary of keywords. Use `PyArg_ParseTupleAndKeywords()` to parse the arguments to such a function.

The `PyArg_ParseTupleAndKeywords()` function is declared as follows:

    int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
                                    const char *format, char * const *kwlist, ...);

The arg and format parameters are identical to those of the `PyArg_ParseTuple()` function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a `NULL`-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success, `PyArg_ParseTupleAndKeywords()` returns true, otherwise it returns false and raises an appropriate exception.

Informacja

Zagnieżdźone krotki nie mogą być wczytane gdy używane są parametry słów kluczowych! Parametry słów kluczowych przekazane do zadania które nie są obecne na liście kwlist spowodują że wyjątek `TypeError` zostanie zgłoszony.

Tu jest przykładowy moduł który używa słów kluczowych, oparty na przykładzie Geoffa Philbricka (philbrick@hks.com):

    #define PY_SSIZE_T_CLEAN
    #include <Python.h>

    static PyObject *
    keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
    {
        int voltage;
        const char *state = "a stiff";
        const char *action = "voom";
        const 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_RETURN_NONE;
    }

    static PyMethodDef keywdarg_methods[] = {
        /* The cast of the function is necessary since PyCFunction values
         * only take two PyObject* parameters, and keywdarg_parrot() takes
         * three.
         */
        {"parrot", (PyCFunction)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
         "Print a lovely skit to standard output."},
        {NULL, NULL, 0, NULL}   /* sentinel */
    };

## Budowanie dowolnych wartości

This function is the counterpart to `PyArg_ParseTuple()`. It is declared as follows:

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

It recognizes a set of format units similar to the ones recognized by `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 `PyArg_ParseTuple()`: while the latter requires its first argument to be a tuple (since Python argument lists are always represented as tuples internally), `Py_BuildValue()` does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns `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.

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

    Py_BuildValue("")                        None
    Py_BuildValue("i", 123)                  123
    Py_BuildValue("iii", 123, 456, 789)      (123, 456, 789)
    Py_BuildValue("s", "hello")              'hello'
    Py_BuildValue("y", "hello")              b'hello'
    Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
    Py_BuildValue("s#", "hello", 4)          'hell'
    Py_BuildValue("y#", "hello", 4)          b'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))

## Liczby odniesień

In languages like C or C++, the programmer is responsible for dynamic allocation and deallocation of memory on the heap. In C, this is done using the functions `malloc()` and `free()`. In C++, the operators `new` and `delete` are used with essentially the same meaning and we’ll restrict the following discussion to the C case.

Every block of memory allocated with `malloc()` should eventually be returned to the pool of available memory by exactly one call to `free()`. It is important to call `free()` at the right time. If a block’s address is forgotten but `free()` is not called for it, the memory it occupies cannot be reused until the program terminates. This is called a memory leak. On the other hand, if a program calls `free()` for a block and then continues to use the block, it creates a conflict with reuse of the block through another `malloc()` call. This is called using freed memory. It has the same bad consequences as referencing uninitialized data — core dumps, wrong results, mysterious crashes.

Typowymi przyczynami wycieków pamięci są nietypowe ścieżki przejścia przez kod. Dla przykładu, zadanie może zaalokować blok pamięci, wykonać pewne obliczenia, a potem uwolnić ten blok jeszcze raz. Teraz zmiana w wymaganiach dla zadania może dodać test do obliczenia który wykrywa warunek błędu i może wrócić wcześniej z zadania. Łatwo jest zapomnieć aby uwolnić zaalokowany blok pamięci podczas wybierania tej drogi wcześniejszego zakończenia, szczególnie gdy jest dodawane później do kodu. Takie przecieki, gdy raz wprowadzone, często uchodzą niewykryte przez długi czas: błędne wyjście jest wybierane tylko w małym wycinku wszystkich wywołań, i większość nowoczesnych maszyn ma mnóstwo wirtualnej pamięci, tak że wyciek staje się widoczny tylko w długo działającym procesie który używa cieknącego zadania często. Dlatego też, jest to ważne aby zapobiegać wyciekom przed ich nastąpieniem, przez powzięcie konwencji kodowania lub strategii która minimalizuje ten rodzaj błędu.

Since Python makes heavy use of `malloc()` and `free()`, it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called 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 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 `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 `malloc()` and `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.

Podczas gdy język pytonowski używa tradycyjnego wypełnienia zliczania odniesień, on także oferuje wykrywanie cykli, które pracuje aby wykrywać cykliczne odniesienia. To pozwala aplikacjom nie martwić się o tworzenie bezpośrednich lub pośrednich cyklicznych odniesień; to są słabości wypełnienia zbiórki śmieci opartego jedynie na zliczaniu odniesień. Cykle odniesień składają się z przedmiotów które zawierają (możliwie pośrednio) odniesienia do samych siebie, tak że każdy przedmiot w cyklu ma liczbę odniesień która jest nie-zerowa. Typowe wypełnienia zliczające odniesienia nie są w stanie przejąć z powrotem pamięci należącej do któregokolwiek z przedmiotów w cyklu odniesień, ani do której odnosi się któryś z przedmiotów w cyklu, nawet jeśli nie ma więcej odniesień do cyklu samego w sobie.

The cycle detector is able to detect garbage cycles and can reclaim them. The `gc` module exposes a way to run the detector (the `collect()` function), as well as configuration interfaces and the ability to disable the detector at runtime.

### Zliczanie odniesień w języku pytonowskim

There are two macros, `Py_INCREF(x)` and `Py_DECREF(x)`, which handle the incrementing and decrementing of the reference count. `Py_DECREF()` also frees the object when the count reaches zero. For flexibility, it doesn’t call `free()` directly — rather, it makes a call through a function pointer in the object’s 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 `Py_INCREF(x)` and `Py_DECREF(x)`? Let’s first introduce some terms. Nobody „owns” an object; however, you can 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 `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 `Py_DECREF()`. Forgetting to dispose of an owned reference creates a memory leak.

It is also possible to borrow [1] a reference to an object. The borrower of a reference should not call `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 [2].

Zaletą pożyczania ponad posiadaniem odniesienia jest to że nie potrzebujesz zaprzątać swojej uwagi pozbyciem się odniesienia na wszystkich możliwych ścieżkach przejścia przez kod — innymi słowy, z pożyczonym odniesieniem nie musisz ryzykować wycieku gdy nastąpi przedwczesne wyjście z programu. Wadą pożyczania ponad posiadaniem jest to że istnieją pewne szczególne sytuacje gdzie w wydawałoby się poprawnym kodzie pożyczone odniesienie może być użyte po tym jak właściciel od którego zostało ono pożyczone faktycznie pozbył się go.

A borrowed reference can be changed into an owned reference by calling `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 (the new owner must dispose of the reference properly, as well as the previous owner).

### Zasady właścicielskie

Zawsze gdy odniesienie do przedmiotu jest przekazywane do lub z zadania, jest częścią specyfiki sprzęgu zadania to czy własność jest przekazywana z odniesieniem czy też nie.

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, such as `PyLong_FromLong()` and `Py_BuildValue()`, pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, `PyLong_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 `PyObject_GetAttrString()`. The picture is less clear, here, however, since a few common routines are exceptions: `PyTuple_GetItem()`, `PyList_GetItem()`, `PyDict_GetItem()`, and `PyDict_GetItemString()` all return references that you borrow from the tuple, list or dictionary.

The function `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 `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 `Py_INCREF()` to become an independent owner. There are exactly two important exceptions to this rule: `PyTuple_SetItem()` and `PyList_SetItem()`. These functions take over ownership of the item passed to them — even if they fail! (Note that `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 `Py_INCREF()`.

Odniesienie do przedmiotu zwrócone z zadania C które jest wywołane z poziomu języka pytonowskiego musi być posiadanym odniesieniem — prawo własności jest przekazywane z zadania do wywołującego to ostatnie.

### Cienki lód

Istnieje kilka sytuacji gdzie wydawałoby się nieszkodliwe użycie pożyczonych odniesień może prowadzić do kłopotów. Wszystkie one mają do czynienia z niejawnymi wezwaniami programu interpretującego polecenia języka pytonowskiego, które mogą powodować że właściciel odniesienia pozbędzie się go.

The first and most important case to know about is using `Py_DECREF()` on an unrelated object while borrowing a reference to a list item. For instance:

    void
    bug(PyObject *list)
    {
        PyObject *item = PyList_GetItem(list, 0);

        PyList_SetItem(list, 1, PyLong_FromLong(0L));
        PyObject_Print(item, stdout, 0); /* BUG! */
    }

To zadanie najpierw pożycza odniesienie do `list[0]`, potem zamienia `list[1]` na wartość `0`, i ostatecznie wypisuje pożyczone odniesienie. Wydaje się nieszkodliwe, czyż nie? A jednak jest!

Let’s follow the control flow into `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 `__del__()` method. If this class instance has a reference count of 1, disposing of it will call its `__del__()` method. Internally, `PyList_SetItem()` calls `Py_DECREF()` on the replaced item, which invokes replaced item’s corresponding `tp_dealloc` function. During deallocation, `tp_dealloc` calls `tp_finalize`, which is mapped to the `__del__()` method for class instances (see PEP 442). This entire sequence happens synchronously within the `PyList_SetItem()` call.

Since it is written in Python, the `__del__()` method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to `item` in `bug()`? You bet! Assuming that the list passed into `bug()` is accessible to the `__del__()` method, it could execute a statement to the effect of `del list[0]`, and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating `item`.

Rozwiązanie, gdy znasz już źródło problemu, jest łatwe: tymczasowo zwiększyć ilość odniesień. Poprawna wersja zadania równa jest:

    void
    no_bug(PyObject *list)
    {
        PyObject *item = PyList_GetItem(list, 0);

        Py_INCREF(item);
        PyList_SetItem(list, 1, PyLong_FromLong(0L));
        PyObject_Print(item, stdout, 0);
        Py_DECREF(item);
    }

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 `__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 `Py_BEGIN_ALLOW_THREADS`, and to re-acquire it using `Py_END_ALLOW_THREADS`. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:

    void
    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! */
    }

### Puste wskaźniki (NULL)

In general, functions that take object references as arguments do not expect you to pass them `NULL` pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return `NULL` only to indicate that an exception occurred. The reason for not testing for `NULL` arguments is that functions often pass the objects they receive on to other function — if each function were to test for `NULL`, there would be a lot of redundant tests and the code would run more slowly.

It is better to test for `NULL` only at the „source:” when a pointer that may be `NULL` is received, for example, from `malloc()` or from a function that may raise an exception.

The macros `Py_INCREF()` and `Py_DECREF()` do not check for `NULL` pointers — however, their variants `Py_XINCREF()` and `Py_XDECREF()` do.

The macros for checking for a particular object type (`Pytype_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 (`args` in the examples) is never `NULL` — in fact it guarantees that it is always a tuple [3].

It is a severe error to ever let a `NULL` pointer „escape” to the Python user.

## Pisanie rozszerzeń w C++

Jest możliwe pisanie modułów rozszerzających w C++. Niektóre ograniczenia obowiązują. Jeśli główny program (program interpretujący polecenia języka pytonowskiego) jest kompilowany i łączony przez kompilator języka C, nadrzędne lub statyczne przedmioty z konstruktorami nie mogą być używane. To nie jest problemem jeśli główny program jest łączony przez kompilator C++. Zadania które będą wezwane przez program interpretujący polecenia języka pytonowskiego (w szczególności, zadania inicjujące moduł) muszą być deklarowane używając `extern "C"`. Nie jest to konieczne aby zawierać plik nagłówkowy języka pytonowskiego w `extern "C" {...}` — one używają już tej formy jeśli symbol `__cplusplus` jest zdefiniowany (wszystkie niedawne kompilatory C++ definiują ten symbol).

## Dostarczanie sprzęgu programowania aplikacji (API) języka C dla modułu rozszerzającego

Wiele modułów rozszerzających po prostu dostarcza nowych zadań i typów aby były używane z języka pytonowskiego, ale czasami kod w module rozszerzającym może być użyteczny dla innych rozszerzających modułów. Na przykład, moduł rozszerzający mógłby wypełniać typ „kolekcji” który działałby jak lista bez wprowadzonego porządku. Tak jak standardowy typ listy języka pytonowskiego posiada sprzęg programowania aplikacji języka C, który pozwala modułom rozszerzającym tworzenie i zmianę list, ten nowy typ kolekcji powinien mieć zbiór zadań C dla bezpośrednich zmian z innych modułów rozszerzających.

Na pierwszy rzut oka to wydaje się proste: napisać zadania (bez deklarowania ich jako `statycznych`, oczywiście), dostarczyć odpowiedni plik nagłówkowy, i udokumentować API języka C. I faktycznie to mogłoby zadziałać jeśli wszystkie rozszerzające moduły byłyby zawsze złączone statycznie z interpreterem Pythona. Gdy moduły są używane jako współdzielone biblioteki, jednakże, symbole zdefiniowane w jednym module mogą nie być widoczne dla innych modułów. Szczegóły widoczności zależą od systemu operacyjnego; niektóre systemy używają jednej nadrzędnej przestrzeni nazw dla interpretera Pythona i wszystkich modułów rozszerzających (dla Windows, na przykład), podczas gdy inne wymagają jawnej listy importowanych symboli w czasie łączenia modułów (AIX jest jednym z przykładów), lub oferują wybór różnych strategii (większość Unix-ów). I nawet jeśli symbole są widoczne nadrzędnie, moduł którego zadania ktoś chciałby uruchomić mogły nie zostać jeszcze załadowane!

Portability therefore requires not to make any assumptions about symbol visibility. This means that all symbols in extension modules should be declared `static`, except for the module’s initialization function, in order to avoid name clashes with other extension modules. And it means that symbols that should be accessible from other extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (void*). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module’s namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.

Istnieje wiele sposobów w jakie kapsuły mogą być używane aby wystawiać na zewnątrz sprzęgi programowania aplikacji (API) języka C dla danego modułu rozszerzającego. Każde zadanie mogłoby dostać swoją własną kapsułę, lub wszystkie wskaźniki sprzęgu programowania aplikacji (API) języka C mogłyby być zachowane w tabeli której adres byłby opublikowany w kapsule. A różne zadania zachowania i odbioru wskaźników mogłyby być rozprowadzone na różne sposoby pomiędzy moduły dostarczające kod i moduły odbierające.

Whichever method you choose, it’s important to name your Capsules properly. The function `PyCapsule_New()` takes a name parameter (const char*); you’re permitted to pass in a `NULL` name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.

W szczególności, kapsułom używanym do wystawiania sprzęgów programowania aplikacji języka C ( - z ang. - API) powinna być nadana nazwa stosująca się do następującej konwencji:

    modulename.attributename

The convenience function `PyCapsule_Import()` makes it easy to load a C API provided via a Capsule, but only if the Capsule’s name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of void pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.

The exporting module is a modification of the `spam` module from the tutorial. The function `spam.system()` does not call the C library function `system()` directly, but a function `PySpam_System()`, which would of course do something more complicated in reality (such as adding „spam” to every command). This function `PySpam_System()` is also exported to other extension modules.

The function `PySpam_System()` is a plain C function, declared `static` like everything else:

    static int
    PySpam_System(const char *command)
    {
        return system(command);
    }

The function `spam_system()` is modified in a trivial way:

    static PyObject *
    spam_system(PyObject *self, PyObject *args)
    {
        const char *command;
        int sts;

        if (!PyArg_ParseTuple(args, "s", &command))
            return NULL;
        sts = PySpam_System(command);
        return PyLong_FromLong(sts);
    }

Na początku modułu, zaraz za linią

    #include <Python.h>

muszą być dodane dwie linie:

    #define SPAM_MODULE
    #include "spammodule.h"

The `#define` is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module’s `mod_exec` function must take care of initializing the C API pointer array:

    static int
    spam_module_exec(PyObject *m)
    {
        static void *PySpam_API[PySpam_API_pointers];
        PyObject *c_api_object;

        /* Initialize the C API pointer array */
        PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

        /* Create a Capsule containing the API pointer array's address */
        c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

        if (PyModule_Add(m, "_C_API", c_api_object) < 0) {
            return -1;
        }

        return 0;
    }

Note that `PySpam_API` is declared `static`; otherwise the pointer array would disappear when `PyInit_spam()` terminates!

Większa część pracy jest wykonywana w pliku nagłówkowym `spammodule.h`, który wygląda następująco:

    #ifndef Py_SPAMMODULE_H
    #define Py_SPAMMODULE_H
    #ifdef __cplusplus
    extern "C" {
    #endif

    /* Header file for spammodule */

    /* C API functions */
    #define PySpam_System_NUM 0
    #define PySpam_System_RETURN int
    #define PySpam_System_PROTO (const char *command)

    /* Total number of C API pointers */
    #define PySpam_API_pointers 1

    #ifdef SPAM_MODULE
    /* This section is used when compiling spammodule.c */

    static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

    #else
    /* This section is used in modules that use spammodule's API */

    static void **PySpam_API;

    #define PySpam_System \
     (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

    /* Return -1 on error, 0 on success.
     * PyCapsule_Import will set an exception if there's an error.
     */
    static int
    import_spam(void)
    {
        PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
        return (PySpam_API != NULL) ? 0 : -1;
    }

    #endif

    #ifdef __cplusplus
    }
    #endif

    #endif /* !defined(Py_SPAMMODULE_H) */

All that a client module must do in order to have access to the function `PySpam_System()` is to call the function (or rather macro) `import_spam()` in its `mod_exec` function:

    static int
    client_module_exec(PyObject *m)
    {
        if (import_spam() < 0) {
            return -1;
        }
        /* additional initialization can happen here */
        return 0;
    }

Główną wadą tego podejścia jest to, że plik `spammodule.h` jest raczej skomplikowany. Jednakże podstawowa struktura jest taka sama dla każdego zadania które jest wystawiane na zewnątrz więc trzeba się tego uczyć tylko raz.

Ostatecznie warto wspomnieć, że kapsuły dają dodatkowe możliwości działania, które są szczególnie użyteczne dla umieszczania i zabierania miejsca w pamięci wskaźników zachowywanych w kapsule. Szczegóły są opisane w podręczniku użytkownika API Python/C w rozdziale Capsules i w wypełnieniu programowym kapsuł (plików `Include/pycapsule.h` i `Objects/pycapsule.c` w dystrybucji źródłowej kodu Pythona).

Przypisy

[1]

Metafora „pożyczania” odniesienia nie jest do końca poprawna: właściciel wciąż ma kopię odniesienia.

[2]

Sprawdzanie że liczba odniesień jest przynajmniej 1 nie działa — liczba odniesień sama w sobie może być w uwolnionej pamięci i dlatego może być ponownie użyta dla innego przedmiotu!

[3]

Te gwarancje nie są w mocy gdy używasz „starego” sposobu wywoływania — to jest wciąż znajdowane w dużej części istniejącego kodu.